Advances in Word Sense Disambiguation Tutorial at ACL 2005 June 25, 2005 Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse Rada Mihalcea University of North Texas http://www.cs.unt.edu/~rada.

Transcript Advances in Word Sense Disambiguation Tutorial at ACL 2005 June 25, 2005 Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse Rada Mihalcea University of North Texas http://www.cs.unt.edu/~rada.

Advances in Word Sense Disambiguation

Tutorial at ACL 2005 June 25, 2005 Ted Pedersen University of Minnesota, Duluth http://www.d.umn.edu/~tpederse Rada Mihalcea University of North Texas http://www.cs.unt.edu/~rada

Goal of the Tutorial

• • • •

Introduce the problem of word sense disambiguation (WSD), focusing on the range of formulations and approaches currently practiced.

Accessible to anyone with an interest in NLP. Persuade you to work on word sense disambiguation – – It’s an interesting problem Lots of good work already done, still more to do – There is infrastructure to help you get started Persuade you to use word sense disambiguation in your text applications.

Outline of Tutorial

• • • • • • • •

Introduction (Ted) Methodolodgy (Rada) Knowledge Intensive Methods (Rada) Supervised Approaches (Ted) Minimally Supervised Approaches (Rada) / BREAK Unsupervised Learning (Ted) How to Get Started (Rada) Conclusion (Ted)

Part 1: Introduction

Outline

• • • • • Definitions

Ambiguity for Humans and Computers Very Brief Historical Overview Theoretical Connections Practical Applications

Definitions

•

Word sense disambiguation

is the problem of selecting a sense for a word from a set of predefined possibilities. – – Sense Inventory usually comes from a dictionary or thesaurus.

Knowledge intensive methods, supervised learning, and (sometimes) bootstrapping approaches

•

Word sense discrimination

is the problem of dividing the usages of a word into different meanings, without regard to any particular existing sense inventory.

– Unsupervised techniques

Outline

• • • • •

Definitions

Ambiguity for Humans and Computers

Very Brief Historical Overview Theoretical Connections Practical Applications

Computers versus Humans

• • •

Polysemy

– most words have many possible meanings.

A computer program has no basis for knowing which one is appropriate, even if it is obvious to a human… Ambiguity is rarely a problem for humans in their day to day communication, except in extreme cases…

Ambiguity for Humans - Newspaper Headlines!

• • • • • • • • •

DRUNK GETS NINE YEARS IN VIOLIN CASE FARMER BILL DIES IN HOUSE PROSTITUTES APPEAL TO POPE STOLEN PAINTING FOUND BY TREE RED TAPE HOLDS UP NEW BRIDGE DEER KILL 300,000 RESIDENTS CAN DROP OFF TREES INCLUDE CHILDREN WHEN BAKING COOKIES MINERS REFUSE TO WORK AFTER DEATH

Ambiguity for a Computer

• • • • •

The fisherman jumped off the

bank

and into the water.

The

bank

down the street was robbed!

Back in the day, we had an entire

bank

of computers devoted to this problem. The

bank

in that road is entirely too steep and is really dangerous. The plane took a

bank

to the left, and then headed off towards the mountains.

Outline

• • • • •

Definitions Ambiguity for Humans and Computers

Very Brief Historical Overview

Theoretical Connections Practical Applications

Early Days of WSD

• •

Noted as problem for Machine Translation (Weaver, 1949) – A word can often only be translated if you know the specific sense intended (A bill in English could be a pico or a cuenta in Spanish) Bar-Hillel (1960) posed the following: – – Little John was looking for his toy box. Finally, he found it. The box was in the pen. John was very happy.

Is “pen” a writing instrument or an enclosure where children play?

…declared it unsolvable, left the field of MT!

Since then…

• • •

1970s - 1980s – – Rule based systems Rely on hand crafted knowledge sources 1990s – Corpus based approaches – – Dependence on sense tagged text (Ide and Veronis, 1998) overview history from early days to 1998. 2000s – – – Hybrid Systems Minimizing or eliminating use of sense tagged text Taking advantage of the Web

Outline

• • • • •

Definitions Ambiguity for Humans and Computers Very Brief Historical Overview

Interdisciplinary Connections

Practical Applications

Interdisciplinary Connections

• • •

Cognitive Science & Psychology – Quillian (1968), Collins and Loftus (1975) : spreading activation • Hirst (1987) developed marker passing model Linguistics – Fodor & Katz (1963) : selectional preferences • Resnik (1993) pursued statistically Philosophy of Language – – Wittgenstein (1958): meaning as use “For a

large

class of cases-though not for all-in which we employ the word "meaning" it can be defined thus: the meaning of a word is its use in the language.”

Outline

• • • • •

Definitions Ambiguity for Humans and Computers Very Brief Historical Overview Theoretical Connections

Practical Applications

• • • •

Machine Translation – Translate “bill” from English to Spanish • • Is it a “pico” or a “cuenta”?

Is it a bird jaw or an invoice?

Information Retrieval – Find all Web Pages about “cricket” • The sport or the insect?

Question Answering – What is George Miller’s position on gun control?

• The psychologist or US congressman?

Knowledge Acquisition – Add to KB: Herb Bergson is the mayor of Duluth.

• Minnesota or Georgia?

References

• • • • • • • • • (Bar-Hillel, 1960) The Present Status of Automatic Translations of Languages. In Advances in Computers. Volume 1. Alt, F. (editor). Academic Press, New York, NY. pp 91-163. (Collins and Loftus, 1975) A Spreading Activation Theory of Semantic Memory. Psychological Review, (82) pp. 407-428. (Fodor and Katz, 1963) The structure of semantic theory. Language (39). pp 170-210. (Hirst, 1987) Semantic Interpretation and the Resolution of Ambiguity. Cambridge University Press. (Ide and Véronis, 1998)Word Sense Disambiguation: The State of the Art.

Computational Linguistics (24

)

pp 1-40.

(Quillian, 1968) Semantic Memory. In Semantic Information Processing. Minsky, M. (editor). The MIT Press, Cambridge, MA. pp. 227-270. (Resnik, 1993) Selection and Information: A Class-Based Approach to Lexical Relationships. Ph.D. Dissertation. University of Pennsylvania. (Weaver, 1949): Translation. In Machine Translation of Languages: fourteen essays. Locke, W.N. and Booth, A.D. (editors) The MIT Press, Cambridge, Mass. pp. 15-23. (Wittgenstein, 1958) Philosophical Investigations, 3 rd Anscombe. Macmillan Publishing Co., New York.

edition. Translated by G.E.M.

Part 2: Methodology

Outline

• • • • •

General considerations All-words disambiguation Targeted-words disambiguation Word sense discrimination, sense discovery Evaluation (granularity, scoring)

21 •

Overview of the Problem

Many words have several meanings (homonymy / polysemy) – Ex: “ chair ” – furniture or person – Ex: “ child ” – young person or human offspring • • Determine which sense of a word is used in a specific sentence

Note:

– often, the different senses of a word are closely related • Ex: title - right of legal ownership - document that is evidence of the legal ownership, – sometimes, several senses can be “activated” in a single context (co-activation) • Ex:

“This could bring competition to the trade”

competition : - the act of competing - the people who are competing

Word Senses

•

The

meaning

of a word in a given context 22

•

Word sense representations – With respect to a dictionary

chair

= a seat for one person, with a support for the back; "he put his coat over the back of the chair and sat down"

chair

= the position of professor; "he was awarded an endowed chair in economics" – With respect to the translation in a second language chair = chaise chair = directeur – With respect to the context where it occurs (discrimination) “Sit on a chair ” “Take a seat on this chair ” “The chair of the Math Department” “The chair of the meeting”

Approaches to Word Sense Disambiguation

• • •

Knowledge-Based Disambiguation – use of external lexical resources such as dictionaries and thesauri – discourse properties Supervised Disambiguation – based on a labeled training set – the learning system has: • a training set of feature-encoded inputs AND • their appropriate sense label (category) Unsupervised Disambiguation – based on unlabeled corpora – The learning system has: • a training set of feature-encoded inputs BUT • NOT their appropriate sense label (category)

All Words Word Sense Disambiguation

•

Attempt to disambiguate all open-class words in a text “He put his suit over the back of the chair ”

• • • •

Knowledge-based approaches Use information from dictionaries – Definitions / Examples for each meaning • Find similarity between definitions and current context Position in a semantic network • Find that “ table ” is closer to “ chair/furniture ” than to “ chair/person ” Use discourse properties • A word exhibits the same sense in a discourse / in a collocation

All Words Word Sense Disambiguation

• •

Minimally supervised approaches – – Learn to disambiguate words using small annotated corpora E.g. SemCor – corpus where all open class words are disambiguated • 200,000 running words Most frequent sense

Targeted Word Sense Disambiguation

• Disambiguate one target word “Take a seat on this chair ” “The chair of the Math Department” • • • WSD is viewed as a typical classification problem – use machine learning techniques to train a system Training: – Corpus of occurrences of the target word, each occurrence annotated with appropriate sense – Build feature vectors: • a vector of relevant linguistic features that represents the context (ex: a window of words around the target word) Disambiguation: – Disambiguate the target word in new unseen text

Targeted Word Sense Disambiguation

• • Take a window of

word around the target word Encode information about the words around the target word – typical features include: words, root forms, POS tags, frequency, … • An electric guitar and

bass

player stand off to one side, not really part of the scene, just as a sort of nod to gringo expectations perhaps.

• Surrounding context (local features) – [ (guitar, NN1), (and, CJC), (player, NN1), (stand, VVB) ] • Frequent co-occurring words (topical features) – – [

fishing, big, sound, player, fly, rod, pound, double, runs, playing, guitar, band

] [0,0,0,1,0,0,0,0,0,0,1,0] • Other features: – [followed by "player", contains "show" in the sentence,…] – [yes, no , … ]

Unsupervised Disambiguation

• • • Disambiguate word senses: – – without supporting tools such as dictionaries and thesauri without a labeled training text Without such resources, word senses are not

labeled

– We cannot say “ chair/furniture” or “ chair/person” We can: – Cluster/group the contexts of an ambiguous word into a number of groups –

Discriminate

between these groups without actually labeling them

Unsupervised Disambiguation

• • Hypothesis: same senses of words will have similar neighboring words Disambiguation algorithm – Identify context vectors corresponding to all occurrences of a particular word – – Partition them into regions of high density Assign a sense to each such region “Sit on a chair ” “Take a seat on this chair ” “The chair of the Math Department” “The chair of the meeting”

Evaluating Word Sense Disambiguation

• Metrics: – Precision = percentage of words that are tagged correctly, out of the words addressed by the system – Recall = percentage of words that are tagged correctly, out of all words in the test set – Example • • Test set of 100 words System attempts 75 words • Words correctly disambiguated 50 Precision = 50 / 75 = 0.66

Recall = 50 / 100 = 0.50

• • Special tags are possible: – – – Unknown Proper noun Multiple senses Compare to a gold standard – SEMCOR corpus, SENSEVAL corpus, …

Evaluating Word Sense Disambiguation

• • Difficulty in evaluation: – Nature of the senses to distinguish has a huge impact on results Coarse versus fine-grained sense distinction

chair

= a seat for one person, with a support for the back; "he put his coat over the back of the chair and sat down“

chair

= the position of professor ; "he was awarded an endowed chair in economics“ •

bank

= a financial institution that accepts deposits and channels the money into lending activities; "he cashed a check at the bank"; "that bank holds the mortgage on my home"

bank

= a building in which commercial banking is transacted; "the bank is on the corner of Nassau and Witherspoon“ Sense maps – – Cluster similar senses Allow for both fine-grained and coarse-grained evaluation

Bounds on Performance

• Upper and Lower Bounds on Performance: – Measure of how well an algorithm performs relative to the difficulty of the task.

• Upper Bound: – – – Human performance Around 97%-99% with few and clearly distinct senses Inter-judge agreement: • With words with clear & distinct senses – 95% and up • With polysemous words with related senses – 65% – 70% • Lower Bound (or baseline): – The assignment of a random sense / the most frequent sense • 90% is excellent for a word with 2 equiprobable senses • 90% is trivial for a word with 2 senses with probability ratios of 9 to 1

33 • • • •

References

(Gale, Church and Yarowsky 1992) Gale, W., Church, K., and Yarowsky, D.

Estimating upper and lower bounds on the performance of word-sense disambiguation programs

ACL 1992

(Miller et. al., 1994) Miller, G., Chodorow, M., Landes, S., Leacock, C., and Thomas, R.

Using a semantic concordance for sense identification

. ARPA Workshop 1994.

(Miller, 1995) Miller, G. Wordnet: A lexical database. ACM, 38(11) 1995.

(Senseval) Senseval evaluation exercises http://www.senseval.org

Part 3: Knowledge-based Methods for Word Sense Disambiguation

Outline

• • • • • Task definition

– Machine Readable Dictionaries Algorithms based on Machine Readable Dictionaries Selectional Restrictions Measures of Semantic Similarity Heuristic-based Methods

Task Definition

• • • Knowledge-based WSD

= class of WSD methods relying (mainly) on knowledge drawn from dictionaries and/or raw text Resources – Yes • • Machine Readable Dictionaries Raw corpora – No • Manually annotated corpora Scope – All open-class words

Machine Readable Dictionaries

• • •

In recent years, most dictionaries made available in Machine Readable format (MRD) – Oxford English Dictionary – – Collins Longman Dictionary of Ordinary Contemporary English (LDOCE) Thesauruses – add synonymy information – Roget Thesaurus Semantic networks – add more semantic relations – – WordNet EuroWordNet

MRD – A Resource for Knowledge-based WSD

•

For each word in the language vocabulary, an MRD provides: – A list of meanings – – Definitions (for all word meanings) Typical usage examples (for most word meanings) WordNet definitions/examples for the noun

plant

buildings for carrying on industrial labor; "they built a large plant to manufacture automobiles“ a living organism lacking the power of locomotion something planted secretly for discovery by another; "the police used a plant to trick the thieves"; "he claimed that the evidence against him was a plant" an actor situated in the audience whose acting is rehearsed but seems spontaneous to the audience

MRD – A Resource for Knowledge-based WSD

•

A thesaurus adds: – An explicit synonymy relation between word meanings WordNet synsets for the noun “plant” 1. plant, works, industrial plant 2. plant, flora, plant life

•

A semantic network adds: – Hypernymy/hyponymy (IS-A), meronymy/holonymy (PART-OF), antonymy, entailnment, etc.

WordNet related concepts for the meaning “plant life” {plant, flora, plant life} hypernym: {organism, being} hypomym: {house plant}, {fungus}, … meronym: {plant tissue}, {plant part} holonym: {Plantae, kingdom Plantae, plant kingdom}

Outline

• • •

Task definition – Machine Readable Dictionaries

• • Algorithms based on Machine Readable Dictionaries

Selectional Restrictions Measures of Semantic Similarity Heuristic-based Methods

Lesk Algorithm

•

(Michael Lesk 1986): Identify senses of words in context using definition overlap

Algorithm:

1. Retrieve from MRD all sense definitions of the words to be disambiguated 2. Determine the definition overlap for all possible sense combinations 3. Choose senses that lead to highest overlap • Example: disambiguate PINE CONE • PINE 1. kinds of evergreen tree with needle-shaped leaves 2. waste away through sorrow or illness CONE 1. solid body which narrows to a point 2. something of this shape whether solid or hollow 3. fruit of certain evergreen trees Pine#1  Pine#2  Pine#1  Pine#2  Pine#1  Pine#2  Cone#1 = 0 Cone#1 = 0 Cone#2 = 1 Cone#2 = 0 Cone#3 = 2 Cone#3 = 0

Lesk Algorithm for More than Two Words?

•

I saw a man who is 98 years old and can still walk and tell jokes

– nine open class words:

walk (10), tell (8), joke (3) see (26), man (11), year (4), old (8), can (5), still (4),

• • 43,929,600 sense combinations! How to find the optimal sense combination?

Simulated annealing (Cowie, Guthrie, Guthrie 1992) – – Define a function E = combination of word senses in a given text.

Find the combination of senses that leads to highest definition overlap (

redundancy)

1. Start with E = the most frequent sense for each word 2. At each iteration, replace the sense of a random word in the set with a different sense, and measure E 3. Stop iterating when there is no change in the configuration of senses

Lesk Algorithm: A Simplified Version

• • • Original Lesk definition: measure overlap between sense definitions for all words in context – Identify simultaneously the correct senses for all words in context Simplified Lesk (Kilgarriff & Rosensweig 2000): measure overlap between sense definitions of a word and current context – Identify the correct sense for one word at a time Search space significantly reduced

Lesk Algorithm: A Simplified Version

• Algorithm

for simplified Lesk: 1.Retrieve from MRD all sense definitions of the word to be disambiguated 2.Determine the overlap between each sense definition and the current context 3.Choose the sense that leads to highest overlap Example: disambiguate PINE in

“Pine cones hanging in a tree”

• PINE 1. kinds of evergreen tree with needle-shaped leaves 2. waste away through sorrow or illness Pine#1  Pine#2  Sentence = 1 Sentence = 0

Evaluations of Lesk Algorithm

• • • •

Initial evaluation by M. Lesk – 50-70% on short samples of text manually annotated set, with respect to Oxford Advanced Learner’s Dictionary Simulated annealing – 47% on 50 manually annotated sentences Evaluation on Senseval-2 all-words data, with back-off to random sense (Mihalcea & Tarau 2004) – – Original Lesk: 35% Simplified Lesk: 47% Evaluation on Senseval-2 all-words data, with back-off to most frequent sense (Vasilescu, Langlais, Lapalme 2004) – – Original Lesk: 42% Simplified Lesk: 58%

Outline

• • •

Task definition – Machine Readable Dictionaries

•

Algorithms based on Machine Readable Dictionaries

• Selectional Preferences

Measures of Semantic Similarity Heuristic-based Methods

Selectional Preferences

•

A way to constrain the possible meanings of words in a given context

•

E.g. “ Wash a dish ” vs. “ Cook a dish ” – WASH-OBJECT vs. COOK-FOOD

• •

Capture information about possible relations between semantic classes – Common sense knowledge Alternative terminology – Selectional Restrictions – Selectional Preferences – Selectional Constraints

Acquiring Selectional Preferences

•

From annotated corpora – Circular relationship with the WSD problem • • Need WSD to build the annotated corpus Need selectional preferences to derive WSD

•

From raw corpora – Frequency counts – – Information theory measures Class-to-class relations

Preliminaries: Learning Word-to-Word Relations

•

An indication of the

semantic fit between two words 1. Frequency counts

– Pairs of words connected by a syntactic relations

Count

(

1 ,

2 ,

) 2. Conditional probabilities – Condition on one of the words

(

1 |

2 ,

) 

Count

(

1 ,

2 ,

)

Count

(

2 ,

)

Learning Selectional Preferences (1)

•

Word-to-class relations (Resnik 1993) – Quantify the contribution of a semantic class using all the concepts subsumed by that class

(

2 |

1 ,

) log

(

2 |

1 ,

)

(

1 ,

2 ,

)  

(

2 |

1 ,

) log

(

2 )

(

2 |

1 ,

)

(

2 ) – where

(

2 |

1 ,

) 

Count

(

W Count

1 , (

2 , ,

)

Count

(

1 ,

2 ,

) 

2  

Count

(

1 ,

2 ,

)

Count

(

2 )

•

Learning Selectional Preferences (2)

Determine the contribution of a word sense based on the assumption of equal sense distributions: – e.g. “plant” has two senses  50% occurences are sense 1, 50% are sense 2 51

•

Example: learning restrictions for the verb “ to drink ” – Find high-scoring verb-object pairs –

Co-occ score

11.75

10.53

10.2

9.34

Verb

drink tea

Object

drink Pepsi drink champagne drink liquid drink beer drink wine Find “prototypical” object classes (high association score)

A(v,c) Object class

3.58 (beverage, [drink, …]) 2.05 (alcoholic_beverage, [intoxicant, …])

Learning Selectional Preferences (3)

•

Other algorithms

– Learn class-to-class relations (Agirre and Martinez, 2002) • E.g.: “ingest food” is a class-to-class relation for “eat chicken” – – Bayesian networks (Ciaramita and Johnson, 2000) Tree cut model (Li and Abe, 1998)

Using Selectional Preferences for WSD

Algorithm:

1. Learn a large set of selectional preferences for a given syntactic relation R 2. Given a pair of words W 1 – W 2 connected by a relation R 3. Find all selectional preferences W 1 – C (word-to-class) or C 1 – C 2 (class-to-class) that apply 4. Select the meanings of W 1 class and W 2 based on the selected semantic Example: disambiguate

coffee

in “drink

coffee

” 1. (beverage) a beverage consisting of an infusion of ground coffee beans 2. (tree) any of several small trees native to the tropical Old World 3. (color) a medium to dark brown color Given the selectional preference “DRINK BEVERAGE” : coffee#1

Evaluation of Selectional Preferences for WSD

• • •

Data set – mainly on verb-object, subject-verb relations extracted from SemCor Compare against random baseline Results (Agirre and Martinez, 2000) – – Average results on 8 nouns Similar figures reported in (Resnik 1997) Random Word-to-word Word-to-class Class-to-class Object Precision Subject Recall Precision Recall 19.2

95.9

66.9

66.6

19.2

24.9

58.0

64.8

19.2

74.2

56.2

54.0

19.2

18.0

46.8

53.7

Outline

• •

Task definition – Machine Readable Dictionaries

• •

Algorithms based on Machine Readable Dictionaries Selectional Restrictions

• Measures of Semantic Similarity

Heuristic-based Methods

Semantic Similarity

• •

Words in a discourse must be related in meaning, for the discourse to be coherent (Haliday and Hassan, 1976) Use this property for WSD – Identify related meanings for words that share a common context

•

Context span: 1. Local context: semantic similarity between pairs of words 2. Global context: lexical chains

Semantic Similarity in a Local Context

• •

Similarity determined between pairs of concepts, or between a word and its surrounding context Relies on similarity metrics on semantic networks – (Rada et al. 1989) carnivore fissiped mamal, fissiped canine, canid feline, felid bear wolf wild dog dog hyena dingo hyena dog hunting dog dachshund terrier

58 • • •

Semantic Similarity Metrics (1)

Input: two concepts (same part of speech) Output: similarity measure (Leacock and Chodorow 1998) –

Similarity

(

1 ,

2 )   log

Path

(

1 ,

2 ) 2

, D is the taxonomy depth E.g. Similarity( wolf , dog ) = 0.60 Similarity( wolf , bear ) = 0.42

• (Resnik 1995) – Define information content, P(C) = probability of seeing a concept of type C in a large corpus

(

)   log(

(

)) – Probability of seeing a concept = probability of seeing instances of that concept – Determine the contribution of a word sense based on the assumption of equal sense distributions: • e.g. “plant” has two senses  50% occurrences are sense 1, 50% are sense 2

Semantic Similarity Metrics (2)

• Similarity using information content – (Resnik 1995) Define similarity between two concepts (LCS = Least Common Subsumer)

Similarity

(

1 ,

2 ) 

(

LCS

(

1 ,

2 )) • – Alternatives (Jiang and Conrath 1997)

Similarity

(

1 ,

2 ) (

(

1 ) Other metrics:  2 

(

LCS

(

1 ,

2 )) 

(

2  )) – – Similarity using information content (Lin 1998) Similarity using gloss-based paths across different hierarchies (Mihalcea and Moldovan 1999) – Conceptual density measure between noun semantic hierarchies and current context (Agirre and Rigau 1995) – Adapted Lesk algorithm (Banerjee and Pedersen 2002)

Semantic Similarity Metrics for WSD

•

Disambiguate target words based on similarity with one word to the left and one word to the right – (Patwardhan, Banerjee, Pedersen 2002) Example: disambiguate PLANT in “plant with flowers” PLANT 1.

plant, works, industrial plant 2.

plant, flora, plant life Similarity (plant#1, flower) = 0.2

Similarity (plant#2, flower) = 1.5 : plant#2

•

Evaluation: – – 1,723 ambiguous nouns from Senseval-2 Among 5 similarity metrics, (Jiang and Conrath 1997) provide the best precision (39%)

Semantic Similarity in a Global Context

• • Lexical chains (Hirst and St-Onge 1988), (Haliday and Hassan 1976) “

A lexical chain is a

equence of semantically related words, which creates a context and contributes to the continuity of meaning and the coherence of a discourse

”

Algorithm

for finding lexical chains: 1.

Select the candidate words from the text. These are words for which we can compute similarity measures, and therefore most of the time they have the same part of speech.

For each such candidate word, and for each meaning for this word, find a chain to receive the candidate word sense, based on a semantic relatedness measure between the concepts that are already in the chain, and the candidate word meaning.

If such a chain is found, insert the word in this chain; otherwise, create a new chain.

Semantic Similarity of a Global Context

A very long

train traveling

certain

direction

… along the

rails

with a constant

velocity

v in a train #1: public transport #2: order set of things #3: piece of cloth #1 change location # 2: a bar of steel for trains travel #2: undergo transportation rail #1: a barrier #3: a small bird

Lexical Chains for WSD

• •

Identify lexical chains in a text – Usually target one part of speech at a time Identify the meaning of words based on their membership to a lexical chain 63

•

Evaluation: – (Galley and McKeown 2003) lexical chains on 74 SemCor texts give 62.09% – (Mihalcea and Moldovan 2000) on five SemCor texts give 90% with 60% recall • lexical chains “anchored” on monosemous words – (Okumura and Honda 1994) lexical chains on five Japanese texts give 63.4%

Outline

•

Task definition – Machine Readable Dictionaries

• •

Algorithms based on Machine Readable Dictionaries Selectional Restrictions

•

Measures of Semantic Similarity

• Heuristic-based Methods

Most Frequent Sense (1)

• •

Identify the most often used meaning and use this meaning by default Example: “plant/flora” is used more often than annotate any instance of PLANT as “plant/factory” “plant/flora” Word meanings exhibit a Zipfian distribution – E.g. distribution of word senses in SemCor 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 1 2 3 4 5 6 Sense number 7 8 9 10 Noun Verb Adj Adv

• •

Most Frequent Sense (2)

Method 1: Find the most frequent sense in an annotated corpus Method 2: Find the most frequent sense using a method based on distributional similarity (McCarthy et al. 2004) 1. Given a word

, find the top

distributionally similar words N w = {n 1 , n 2 , …, n k }, with associated similarity scores {dss(w,n 1 ), dss(w,n 2 ), … dss(w,n k )} 2. For each sense ws i of w, identify the similarity with the words n j , using the sense of n j that maximizes this score 3. Rank senses ws i of w based on the total similarity score

Score

(

ws i

) 

n j

 

N w dss

(

n j

)

ws i wnss w

) (

ws i

 ' 

senses

(

wnss

( ,

n ws i j

' ) ,

n j

) , where

wnss

(

ws i

n j

) 

ns x

max 

senses

(

n j

) (

wnss

(

ws i

ns x

))

Most Frequent Sense(3)

• • • • Word senses – pipe #1 – pipe #2 = tobacco pipe = tube of metal or plastic Distributional similar words – N = { tube, cable, wire, tank, hole, cylinder, fitting, tap , …} For each word in N, find similarity with pipe#i (using the sense that maximizes the similarity) – pipe#1 – tube – pipe#2 – tube (#3) = 0.3

(#1) = 0.6

Compute score for each sense pipe#i – – score ( pipe#1 ) = 0.25

score ( pipe#2 ) = 0.73 Note: results depend on the corpus used to find distributionally similar words => can find domain specific predominant senses

One Sense Per Discourse

• • • • A word tends to preserve its meaning across all its occurrences in a given discourse (Gale, Church, Yarowksy 1992) What does this mean?

E.g. The ambiguous word PLANT occurs 10 times in a discourse all instances of “plant” carry the same meaning Evaluation: – – 8 words with two-way ambiguity, e.g. plant , crane , etc.

98% of the two-word occurrences in the same discourse carry the same meaning The grain of salt: Performance depends on granularity – – (Krovetz 1998) experiments with words with more than two senses Performance of “one sense per discourse” measured on SemCor is approx. 70%

One Sense per Collocation

69 • • • • A word tends to preserver its meaning when used in the same collocation (Yarowsky 1993) – – Strong for adjacent collocations Weaker as the distance between words increases An example The ambiguous word PLANT preserves its meaning in all its occurrences within the collocation “industrial plant”, regardless of the context where this collocation occurs Evaluation: – 97% precision on words with two-way ambiguity Finer granularity: – (Martinez and Agirre 2000) tested the “one sense per collocation” hypothesis on text annotated with WordNet senses – 70% precision on SemCor words

70 • • • • • • • • • • • •

References

(Agirre and Rigau, 1995) Agirre, E. and Rigau, G.

using conceptual distance

. RANLP 1995

A proposal for word sense disambiguation

(Agirre and Martinez 2001) Agirre, E. and Martinez, D.

preferences

. CONLL 2001.

Learning class-to-class selectional

(Banerjee and Pedersen 2002) Banerjee, S. and Pedersen, T.

word sense disambiguation using WordNet

. CICLING 2002.

An adapted Lesk algorithm for

(Cowie, Guthrie and Guthrie 1992), Cowie, L. and Guthrie, J. A. and Guthrie, L.:

disambiguation using simulated annealing

. COLING 2002.

Lexical

(Gale, Church and Yarowsky 1992) Gale, W., Church, K., and Yarowsky, D.

discourse

. DARPA workshop 1992

One sense per

(Halliday and Hasan 1976) Halliday, M. and Hasan, R., (1976). Cohesion in English.

Longman.

(Galley and McKeown 2003) Galley, M. and McKeown, K. (2003) Improving word sense disambiguation in lexical chaining. IJCAI 2003 (Hirst and St-Onge 1998) Hirst, G. and St-Onge, D.

Lexical chains as representations of context in the detection and correction of malaproprisms

WordNet: An electronic lexical database

, MIT Press.

(Jiang and Conrath 1997) Jiang, J. and Conrath, D.

statistics and lexical taxonomy

. COLING 1997.

Semantic similarity based on corpus

(Krovetz, 1998) Krovetz, R.

More than one sense per discourse

. ACL-SIGLEX 1998.

(Lesk, 1986) Lesk, M.

Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone

. SIGDOC 1986.

(Lin 1998) Lin, D

An information theoretic definition of similarity

. ICML 1998.

71 • • • • • • • • • • • •

References

(Martinez and Agirre 2000) Martinez, D. and Agirre, E.

genre/topic variations

. EMNLP 2000.

One sense per collocation and

(Miller et. al., 1994) Miller, G., Chodorow, M., Landes, S., Leacock, C., and Thomas, R.

Using a semantic concordance for sense identification

. ARPA Workshop 1994.

(Miller, 1995) Miller, G. Wordnet: A lexical database. ACM, 38(11) 1995.

(Mihalcea and Moldovan, 1999) Mihalcea, R. and Moldovan, D.

sense disambiguation of unrestricted text

. ACL 1999.

A method for word

(Mihalcea and Moldovan 2000) Mihalcea, R. and Moldovan, D.

to word sense disambiguation

. FLAIRS 2000.

An iterative approach

(Mihalcea, Tarau, Figa 2004) R. Mihalcea, P. Tarau, E. Figa

PageRank on Semantic Networks with Application to Word Sense Disambiguation,

COLING 2004.

(Patwardhan, Banerjee, and Pedersen 2003) Patwardhan, S. and Banerjee, S. and Pedersen, T.

Using Measures of Semantic Relatedeness for Word Sense Disambiguation

CICLING 2003.

(Rada et al 1989) Rada, R. and Mili, H. and Bicknell, E. and Blettner, M.

Development and application of a metric on semantic nets

. IEEE Transactions on Systems, Man, and Cybernetics, 19(1) 1989.

(Resnik 1993) Resnik, P.

Selection and Information: A Class-Based Approach to Lexical Relationships

. University of Pennsylvania 1993.

(Resnik 1995) Resnik, P.

IJCAI 1995.

Using information content to evaluate semantic similarity

(Vasilescu, Langlais, Lapalme 2004) F. Vasilescu, P. Langlais, G. Lapalme

"Evaluating variants of the Lesk approach for disambiguating words”,

LREC 2004.

(Yarowsky, 1993) Yarowsky, D.

One sense per collocation

. ARPA Workshop 1993.

Part 4: Supervised Methods of Word Sense Disambiguation

Outline

• • • • What is Supervised Learning?

Task Definition Single Classifiers – – Naïve Bayesian Classifiers Decision Lists and Trees Ensembles of Classifiers

What is Supervised Learning?

• • • •

Collect a set of examples that illustrate the various possible classifications or outcomes of an event. Identify patterns in the examples associated with each particular class of the event.

Generalize those patterns into rules.

Apply the rules to classify a new event.

Day

1 2 3 4

Learn from these examples : “when do I go to the store?”

CLASS

Go to Store?

Hot Outside?

Slept Well?

Ate Well?

YES NO YES YES NO NO NO YES YES NO NO NO NO NO NO YES

Day

1 2 3 4

Learn from these examples : “when do I go to the store?”

CLASS

Go to Store?

Hot Outside?

Slept Well?

Ate Well?

YES NO YES YES NO NO NO YES YES NO NO NO NO NO NO YES

Outline

• • • •

What is Supervised Learning?

Task Definition

Single Classifiers – – Naïve Bayesian Classifiers Decision Lists and Trees Ensembles of Classifiers

Task Definition

• • • •

Supervised WSD:

Class of methods that induces a classifier from manually sense-tagged text using machine learning techniques. Resources – – – Sense Tagged Text Dictionary (implicit source of sense inventory) Syntactic Analysis (POS tagger, Chunker, Parser, …) Scope – – – Typically one target word per context Part of speech of target word resolved Lends itself to “targeted word” formulation Reduces WSD to a classification problem where a target word is assigned the most appropriate sense from a given set of possibilities based on the context in which it occurs

Sense Tagged Text

Bonnie and Clyde are two really famous criminals, I think they were

bank/1

robbers My

bank/1

charges too much for an overdraft.

I went to the

bank/1

to deposit my check and get a new ATM card.

The University of Minnesota has an East and a West

Bank/2

campus right on the Mississippi River.

My grandfather planted his pole in the

bank/2

and got a great big catfish! The

bank/2

is pretty muddy, I can’t walk there.

Two Bags of Words (Co-occurrences in the “window of context”)

FINANCIAL_BANK_BAG: a an and are ATM Bonnie card charges check Clyde criminals deposit famous for get I much My new overdraft really robbers the they think to too two went were RIVER_BANK_BAG: a an and big campus cant catfish East got grandfather great has his I in is Minnesota Mississippi muddy My of on planted pole pretty right River The the there University walk West

Simple Supervised Approach

Given a sentence S containing “bank”: For each word W i in S If W i is in FINANCIAL_BANK_BAG then Sense_1 = Sense_1 + 1; If W i is in RIVER_BANK_BAG then Sense_2 = Sense_2 + 1; If Sense_1 > Sense_2 then print “Financial” else if Sense_2 > Sense_1 then print “River” else print “Can’t Decide”;

Supervised Methodology

• • • • • •

Create a sample of

training data

manually annotated

where a given with a sense from a

target word predetermined

set of possibilities.

– One tagged word per instance/lexical sample disambiguation Select a set of features with which to represent context.

– co-occurrences, collocations, POS tags, verb-obj relations, etc... Convert

sense-tagged

training instances to feature vectors.

Apply a machine learning algorithm to induce a classifier. – – Form – structure or relation among features Parameters – strength of feature interactions Convert a

held out

– sample of

test data

into feature vectors.

“correct” sense tags are known but not used Apply classifier to test instances to assign a sense tag.

From Text to Feature Vectors

• •

My/pronoun grandfather/noun used/verb to/prep fish/verb along/adv the/det

banks/SHORE

of/prep the/det Mississippi/noun River/noun. (S1) The/det

bank/FINANCE

issued/verb a/det check/noun for/prep the/det amount/noun of/prep interest/noun. (S2) S1 S2 P-2 adv P-1 det det P+1 prep verb P+2 fish check river interest det det Y N N Y Y N N Y SENSE TAG SHORE FINANCE

Supervised Learning Algorithms

•

Once data is converted to feature vector form, any supervised learning algorithm can be used. Many have – – – – been applied to WSD with good results: – Support Vector Machines – – – – Nearest Neighbor Classifiers Decision Trees Decision Lists Naïve Bayesian Classifiers Perceptrons Neural Networks Graphical Models Log Linear Models

Outline

• • •

What is Supervised Learning?

Task Definition

• • Naïve Bayesian Classifier

Decision Lists and Trees Ensembles of Classifiers

Naïve Bayesian Classifier

• • •

Naïve Bayesian Classifier well known in Machine Learning community for good performance across a range of tasks (e.g., Domingos and Pazzani, 1997) …Word Sense Disambiguation is no exception Assumes

conditional independence

among features, given the sense of a word.

– The

form

of the model is assumed, but parameters are estimated from training instances When applied to WSD, features are often “a bag of words” that come from the training data – Usually thousands of binary features that indicate if a word is present in the context of the target word (or not)

Bayesian Inference

(

|

1 ,

2 ,

3 ,...,

)



(

1 ,

2 ,

3 ,...,

|

)*

(

)

(

1 ,

2 ,

3 ,...,

) • • • •

Given observed features, what is most likely sense?

Estimate probability of observed features given sense Estimate unconditional probability of sense Unconditional probability of features is a normalizing term, doesn’t affect sense classification

Naïve Bayesian Model

S F1 F2 F3 F4 Fn

(

1 ,

2 ,...,

) 

(

1 |

) *

(

2 |

) * ...

(

)

The Naïve Bayesian Classifier

sense



argmax

sense



p

(

F

1 |

S

) * ...

*

p

(

Fn

|

S

) *

p

(

S

)

– – – Given 2,000 instances of “bank”, 1,500 for bank/1 (financial sense) and 500 for bank/2 (river sense) • P(S=1) = 1,500/2000 = .75

• P(S=2) = 500/2,000 = .25

Given “credit” occurs 200 times with bank/1 and 4 times with bank/2.

• P(F1=“credit”) = 204/2000 = .102

• • P(F1=“credit”|S=1) = 200/1,500 = .133

P(F1=“credit”|S=2) = 4/500 = .008

Given a test instance that has one feature “credit” • P(S=1|F1=“credit”) = .133*.75/.102 = .978

• P(S=2|F1=“credit”) = .008*.25/.102 = .020

Comparative Results

• • • •

(Leacock, et. al. 1993) compared Naïve Bayes with a Neural Network and a Context Vector approach when disambiguating six senses of

line…

(Mooney, 1996) compared Naïve Bayes with a Neural Network, Decision Tree/List Learners, Disjunctive and Conjunctive Normal Form learners, and a perceptron when disambiguating six senses of

line

… (Pedersen, 1998) compared Naïve Bayes with Decision Tree, Rule Based Learner, Probabilistic Model, etc. when disambiguating

line

and 12 other words… …All found that Naïve Bayesian Classifier performed as well as any of the other methods!

Outline

• • •

What is Supervised Learning?

Task Definition

•

Naïve Bayesian Classifiers

• Decision Lists and Trees

Ensembles of Classifiers

Decision Lists and Trees

• • • •

Very widely used in Machine Learning. Decision trees used very early for WSD research (e.g., Kelly and Stone, 1975; Black, 1988). Represent disambiguation problem as a series of questions (presence of feature) that reveal the sense of a word.

– – List decides between two senses after one positive answer Tree allows for decision among multiple senses after a series of answers Uses a smaller, more refined set of features than “bag of words” and Naïve Bayes.

– More descriptive and easier to interpret.

Decision List for WSD (Yarowsky, 1994)

• • • •

Identify

collocational

features from sense tagged data. Word immediately to the left or right of target : – – I have my bank/1

statement

The

river

bank/2 is muddy.

Pair of words to immediate left or right of target : – – The

world’s richest

bank/1 is here in New York.

The river bank/2

is muddy.

Words found within k positions to left or right of target, where k is often 10-50 : – My

credit

is just horrible because my bank/1 has made several mistakes with my

account

and the

balance

is very low.

Building the Decision List

• •

Sort order of collocation tests using log of conditional probabilities. Words most indicative of one sense (and not the other) will be ranked highly.

Abs

(log

p p

( (

S S

 1 |

F i

 2 |

F i

 

Collocatio n i Collocatio n i

) ) )

Computing DL score

– – – Given 2,000 instances of “bank”, 1,500 for bank/1 (financial sense) and 500 for bank/2 (river sense) • P(S=1) = 1,500/2,000 = .75

• P(S=2) = 500/2,000 = .25

Given “credit” occurs 200 times with bank/1 and 4 times with bank/2.

• P(F1=“credit”) = 204/2,000 = .102

• • P(F1=“credit”|S=1) = 200/1,500 = .133

P(F1=“credit”|S=2) = 4/500 = .008

From Bayes Rule… • P(S=1|F1=“credit”) = .133*.75/.102 = .978

• P(S=2|F1=“credit”) = .008*.25/.102 = .020

– DL Score = abs (log (.978/.020)) = 3.89

Using the Decision List

•

Sort DL-score, go through test instance looking for matching feature. First match reveals sense… DL-score 3.89

2.20

1.09

0.00

Feature

credit

within bank bank

is muddy pole

within bank

of the

bank Sense Bank/1 financial Bank/2 river Bank/2 river N/A

Using the Decision List

CREDIT?

BANK/1 FINANCIAL BANK/2 RIVER

IS MUDDY? POLE?

BANK/2 RIVER

Learning a Decision Tree

• • • • •

Identify the feature that most “cleanly” divides the training data into the known senses.

– – “Cleanly” measured by information gain or gain ratio. Create subsets of training data according to feature values.

Find another feature that most cleanly divides a subset of the training data.

Continue until each subset of training data is “pure” or as clean as possible.

Well known decision tree learning algorithms include ID3 and C4.5 (Quillian, 1986, 1993) In Senseval-1, a modified decision list (which supported some conditional branching) was most accurate for English Lexical Sample task (Yarowsky, 2000)

Supervised WSD with Individual Classifiers

• • •

Many supervised Machine Learning algorithms have been applied to Word Sense Disambiguation, most work reasonably well. – (Witten and Frank, 2000) is a great intro. to supervised learning.

Features tend to differentiate among methods more than the learning algorithms. Good sets of features tend to include: – Co-occurrences or keywords (global) – Collocations (local) – – – Bigrams (local and global) Part of speech (local) Predicate-argument relations • Verb-object, subject-verb, – Heads of Noun and Verb Phrases

100

Convergence of Results

• •

Accuracy of different systems applied to the same data tends to converge on a particular value, no one system shockingly better than another.

– Senseval-1, a number of systems in range of 74-78% accuracy for English Lexical Sample task.

– Senseval-2, a number of systems in range of 61-64% accuracy for English Lexical Sample task.

– Senseval-3, a number of systems in range of 70-73% accuracy for English Lexical Sample task… What to do next?

101

Outline

• • • •

What is Supervised Learning?

Task Definition Naïve Bayesian Classifiers Decision Lists and Trees

• Ensembles of Classifiers

102

Ensembles of Classifiers

• • •

Classifier error has two components (Bias and Variance) – – Some algorithms (e.g., decision trees) try and build a representation of the training data – Low Bias/High Variance Others (e.g., Naïve Bayes) assume a parametric form and don’t represent the training data – High Bias/Low Variance Combining classifiers with different bias variance characteristics can lead to improved overall accuracy “Bagging” a decision tree can smooth out the effect of small variations in the training data (Breiman, 1996) – Sample with replacement from the training data to learn multiple decision trees.

– Outliers in training data will tend to be obscured/eliminated.

103

Ensemble Considerations

• • •

Must choose different learning algorithms with significantly different bias/variance characteristics.

– Naïve Bayesian Classifier versus Decision Tree Must choose feature representations that yield significantly different (independent?) views of the training data.

– Lexical versus syntactic features Must choose how to combine classifiers. – – – Simple Majority Voting Averaging of probabilities across multiple classifier output Maximum Entropy combination (e.g., Klein, et. al., 2002)

104

Ensemble Results

• • •

(Pedersen, 2000) achieved state of art for

interest

data using ensemble of Naïve Bayesian Classifiers.

and

line

– Many Naïve Bayesian Classifiers trained on varying sized windows of context / bags of words.

– Classifiers combined by a weighted vote (Florian and Yarowsky, 2002) achieved state of the art for Senseval-1 and Senseval-2 data using combination of six classifiers.

– – Rich set of collocational and syntactic features.

Combined via linear combination of top three classifiers.

Many Senseval-2 and Senseval-3 systems employed ensemble methods.

105

References

• • • • • • • • • (Black, 1988) An experiment in computational discrimination of English word senses. IBM Journal of Research and Development (32) pg. 185-194.

(Breiman, 1996) The heuristics of instability in model selection. Annals of Statistics (24) pg. 2350-2383.

(Domingos and Pazzani, 1997) On the Optimality of the Simple Bayesian Classifier under Zero-One Loss, Machine Learning (29) pg. 103-130.

(Domingos, 2000) A Unified Bias Variance Decomposition for Zero-One and Squared Loss. In Proceedings of AAAI. Pg. 564-569. (Florian an dYarowsky, 2002) Modeling Consensus: Classifier Combination for Word Sense Disambiguation. In Proceedings of EMNLP, pp 25-32. (Kelly and Stone, 1975). Computer Recognition of English Word Senses, North Holland Publishing Co., Amsterdam.

(Klein, et. al., 2002) Combining Heterogeneous Classifiers for Word-Sense Disambiguation, Proceedings of Senseval-2. pg. 87-89. (Leacock, et. al. 1993) Corpus based statistical sense resolution. In Proceedings of the ARPA Workshop on Human Language Technology. pg. 260-265. (Mooney, 1996) Comparative experiments on disambiguating word senses: An illustration of the role of bias in machine learning. Proceedings of EMNLP. pg. 82-91.

106

References

• • • • • • • (Pedersen, 1998) Learning Probabilistic Models of Word Sense Disambiguation. Ph.D. Dissertation. Southern Methodist University.

(Pedersen, 2000) A simple approach to building ensembles of Naive Bayesian classifiers for word sense disambiguation. In Proceedings of NAACL. (Quillian, 1986). Induction of Decision Trees. Machine Learning (1). pg. 81-106.

(Quillian, 1993). C4.5 Programs for Machine Learning. San Francisco, Morgan Kaufmann.

(Witten and Frank, 2000). Data Mining – Practical Machine Learning Tools and Techniques with Java Implementations. Morgan-Kaufmann. San Francisco.

(Yarowsky, 1994) Decision lists for lexical ambiguity resolution: Application to accent restoration in Spanish and French. In Proceedings of ACL. pp. 88-95.

(Yarowsky, 2000) Hierarchical decision lists for word sense disambiguation. Computers and the Humanities, 34.

Part 5: Minimally Supervised Methods for Word Sense Disambiguation

108

Outline

• • • Task definition

– What does “minimally” supervised mean?

Bootstrapping algorithms – – – Co-training Self-training Yarowsky algorithm Using the Web for Word Sense Disambiguation – – Web as a corpus Web as collective mind

109

Task Definition

• • Supervised

WSD = learning sense classifiers starting with annotated data

• Minimally supervised

WSD = learning sense classifiers from annotated data, with

minimal

human supervision Examples – Automatically bootstrap a corpus starting with

a few human annotated examples

– Use

monosemous relatives / dictionary definitions

to automatically construct sense tagged data – Rely on

Web-users

+ active learning for corpus annotation

110

Outline

• •

Task definition – What does “minimally” supervised mean?

• Bootstrapping algorithms

– – Co-training Self-training – Yarowsky algorithm Using the Web for Word Sense Disambiguation – – Web as a corpus Web as collective mind

111

Bootstrapping WSD Classifiers

•

Build sense classifiers with little training data – Expand applicability of supervised WSD

•

Bootstrapping approaches – Co-training – – Self-training Yarowsky algorithm

112

Bootstrapping Recipe

• •

Ingredients – (Some) labeled data – – (Large amounts of) unlabeled data (One or more) basic classifiers Output – Classifier that improves over the basic classifiers

113 … plant#1 growth is retarded … … a nuclear power plant#2 … … plants#1 and animals … … industry plant#2 … Classifier 1 Classifier 2 … building the only atomic plant … plant growth is retarded … … a herb or flowering plant … … a nuclear power plant … … building a new vehicle plant … the animal and plant life … … the passion-fruit plant … … …

114

Co-training / Self-training

– A set L of labeled training examples – A set U of unlabeled examples – Classifiers C i

• •

1. Create a pool of examples U' – choose P random examples from U 2. Loop for I iterations – – Train C i on L and label U' Select G most confident examples and add to L • maintain distribution in L – Refill U' with examples from U • keep U' at constant size P

115

Co-training

•

• •

(Blum and Mitchell 1998) Two classifiers – – independent views [independence condition can be relaxed] Co-training in Natural Language Learning – – – – Statistical parsing (Sarkar 2001) Co-reference resolution (Ng and Cardie 2003) Part of speech tagging (Clark, Curran and Osborne 2003) ...

116

Self-training

•

• • •

(Nigam and Ghani 2000) One single classifier Retrain on its own output Self-training for Natural Language Learning – – Part of speech tagging (Clark, Curran and Osborne 2003) Co-reference resolution (Ng and Cardie 2003) • several classifiers through bagging

Parameter Setting for Co-training/Self-training

• •

Pool size Iterations Growth size

117

•

A major drawback of bootstrapping – “No principled method for selecting optimal values for these parameters” (Ng and Cardie 2003)

Experiments with Co-training / Self-training for WSD

• •

Training / Test data – – Senseval-2 nouns (29 ambiguous nouns) Average corpus size: 95 training examples, 48 test examples Raw data – British National Corpus – Average corpus size: 7,085 examples

• •

Co-training – Two classifiers: local and topical classifiers Self-training – One classifier: global classifier 118 • (Mihalcea 2004)

119 • • • • •

Parameter Settings

Parameter ranges – – – P = {1, 100, 500, 1000, 1500, 2000, 5000} G = {1, 10, 20, 30, 40, 50, 100, 150, 200} I = {1, ..., 40} 29 nouns → 120,000 runs Upper bound in co-training/self-training performance – – – – – Optimised on test set Basic classifier: 53.84% Optimal self-training: 65.61% Optimal co-training: 65.75% ~ 25% error reduction • Example: lady – – basic = 61.53% self-training = 84.61% [20/100/39] Per-word parameter setting: – – Co-training = 51.73% Self-training = 52.88% – co-training = 82.05% [1/1000/3] Global parameter setting – – Co-training = 55.67% Self-training = 54.16%

Yarowsky Algorithm

•

• •

(Yarowsky 1995) Similar to co-training Differs in the basic assumption (Abney 2002) – “view independence” (co-training) vs. “precision independence” (Yarowsky algorithm) 120

•

Relies on two heuristics and a decision list – One sense per collocation : • Nearby words provide strong and consistent clues as to the sense of a target word – One sense per discourse : • The sense of a target word is highly consistent within a single document

Learning Algorithm

• A decision list is used to classify instances of target word : “the loss of animal and

plant

species through extinction …” 121

•

Classification is based on the highest ranking rule that matches the target context

LogL

… 9.31 9.24 9.03

9.02

...

Collocation

… flower (within +/- k words) job (within +/- k words) fruit (within +/- k words)

plant species

...

Sense

…  A (living)  B (factory)  A (living) 

A (living)

…

Bootstrapping Algorithm

Sense-A:

life

122 • • All occurrences of the target word are identified A small training set of seed data is tagged with word sense Sense-B:

factory

Bootstrapping Algorithm

123 Seed set grows and residual set shrinks ….

Bootstrapping Algorithm

124 Convergence: Stop when residual set stabilizes

Bootstrapping Algorithm

125

• •

Iterative procedure: – – – Train decision list algorithm on seed set Classify residual data with decision list Create new seed set by identifying samples that are tagged with a probability above a certain threshold – Retrain classifier on new seed set Selecting training seeds – Initial training set should accurately distinguish among possible senses – Strategies: • Select a single, defining seed collocation for each possible sense. Ex: “

life

” and “

manufacturing

” for target

plant

• • Use words from dictionary definitions Hand-label most frequent collocates

Evaluation

126 • • Test corpus: extracted from 460 million word corpus of multiple sources (news articles, transcripts, novels, etc.) Performance of multiple models compared with: – supervised decision lists – unsupervised learning algorithm of Schütze (1992), based on alignment of clusters with word senses

Word plant space tank motion … Avg.

Senses

living/factory volume/outer vehicle/container legal/physical … -

Supervised

97.7

93.9

97.1

98.0

…

96.1

Unsupervised Schütze

92 90 95 92 -

92.2

Unsupervised Bootstrapping

98.6 93.6 96.5 97.9 …

96.5

127

Outline

•

Task definition – What does “minimally” supervised mean?

•

Bootstrapping algorithms – – Co-training Self-training – Yarowsky algorithm

• Using the Web for Word Sense Disambiguation

– – Web as a corpus Web as collective mind

128

The Web as a Corpus

• •

Use the Web as a large textual corpus – – Build annotated corpora using monosemous relatives Bootstrap annotated corpora starting with few seeds • Similar to (Yarowsky 1995) Use the (semi)automatically tagged data to train WSD classifiers

129

Monosemous Relatives

• – Idea

: determine a phrase (SP) which uniquely identifies the sense of a word (W#i) 1.

Determine one or more Search Phrases from a machine readable dictionary using several heuristics Search the Web using the Search Phrases from step 1.

Replace the Search Phrases in the examples gathered at 2 with W#i.

Output: sense annotated corpus for the word sense W#i As a pastime , she enjoyed reading. Evaluate the interestingness of the website.

As an interest , she enjoyed reading.

Evaluate the interest of the website.

130

Heuristics to Identify Monosemous Relatives

• • •

Synonyms

– Determine a monosemous synonym – remember#1 has recollect as monosemous synonym  SP=recollect

Dictionary definitions (1)

– Parse the gloss and determine the set of single phrase definitions – produce#5 has the definition

“bring onto the market or release”

definitions:

“bring onto the market”

and

“release”

eliminate

“release”

as being ambiguous   2 SP=bring onto the market

Dictionary defintions (2)

– – Parse the gloss and determine the set of single phrase definitions Replace the stop words with the NEAR operator – Strengthen the query: concatenate the words from the current synset using the AND operator – produce#6 has the synset {grow, raise, farm, produce}

“cultivate by growing”

 and the definition SP=cultivate NEAR growing AND (grow OR raise OR farm OR produce)

131

Heuristics to Identify Monosemous Relatives

•

Dictionary definitions (3)

– – – Parse the gloss and determine the set of single phrase definitions Keep only the head phrase Strengthen the query: concatenate the words from the current synset using the AND operator – company#5 has the synset {party,company}

people associated in some activity”

 and the definition

“band of

SP=band of people AND (company OR party)

Example

•

132 Building annotated corpora for the noun interest .

# 1 2 3 4 5 6 7 Synset Definition { interest#1 , involvement} { interest#4 } sense of concern with and curiosity about someone or something { interest#2 ,interestingness} the power of attracting or holding one’s interest {sake, interest#3 } reason for wanting something done {pastime, interest#5 } fixed charge for borrowing money; usually a percentage of the amount borrowed a subject or pursuit that occupies one’s time and thoughts { interest#6 , stake} a right or legal share of something; financial involvement with something { interest#7 , interest group} a social group whose members control some field of activity and who have common aims Sense # 1 2 3 4 5 6 7 Search phrase sense of concern AND (interest OR involvement) interestigness reason for wanting AND (interest OR sake) fixed charge AND interest percentage of amount AND interest pastime right share AND (interest OR stake) legal share AND (interest OR stake) financial involvement AND (interest OR stake) interest group

Example

• •

Gather 5,404 examples Check the first 70 examples  67 correct; 95.7% accuracy.

133 1. I appreciate the genuine

interest#1

which motivated you to write your message.

2. The webmaster of this site warrants neither accuracy, nor

interest#2

3. He forgives us not only for our

interest#3

, but for his own.

Interest#4

coverage, including rents, was 3.6x

5. As an

interest#5

, she enjoyed gardening and taking part into church activities.

6. Voted on issues, they should have abstained because of direct and indirect personal

interests#6

in the matters of hand.

7. The Adam Smith Society is a new

interest#7

organized within the APA.

Experimental Evaluation

134

•

Tests on 20 words – – 7 nouns, 7 verbs, 3 adjectives, 3 adverbs (120 word meanings) manually check the first 10 examples of each sense of a word => 91% accuracy (Mihalcea 1999) Word Polysemy count interest report company 7 7 9 school produce 7 7 remember 8 write 8 speak small clearly 4 14 4 TOTAL (20 words) 120 Examples in SemCor 139 71 90 146 148 166 285 147 192 48 2582 Total exam ples acquired 5404 4196 6292 2490 4982 3573 2914 4279 10954 4031 80741 Examples ma nually checked 70 70 80 59 67 67 69 40 107 29 1080 Correct examples 67 63 77 54 60 57 67 39 92 28 978

Web-based Bootstrapping

135 • • Similar to Yarowsky algorithm Relies on data gathered from the Web • 1. Create a set of seeds (phrases) consisting of: – – – Sense tagged examples in SemCor Sense tagged examples from WordNet Additional sense tagged examples, if available Phrase?

– – At least two open class words; Words involved in a semantic relation (e.g. noun phrase, verb-object, verb-subject, etc.) 2. Search the Web using queries formed with the seed expressions found at Step 1 – – Add to the generated corpus of maximum of N text passages Results competitive with manually tagged corpora (Mihalcea 2002)

136

The Web as Collective Mind

• • • •

Two different views of the Web: – – collection of Web pages very large group of Web users Millions of Web users can contribute their knowledge to a data repository Open Mind Word Expert Fast growing rate: – – Started in April 2002 (Chklovski and Mihalcea, 2002) Currently more than 100,000 examples of noun senses in several languages

OMWE online

137

http://teach-computers.org

138

•

• • • •

Open Mind Word Expert: Quantity and Quality

Data – A mix of different corpora: Treebank, Open Mind Common Sense, Los Angeles Times, British National Corpus Word senses – Based on WordNet definitions Active learning – – to select the most informative examples for learning Use two classifiers trained on existing annotated data Select items where the two classifiers disagree for human annotation Quality: – – Two tags per item One tag per item per contributor Evaluations: – – Agreement rates of about 65% - comparable to the agreements rates obtained when collecting data for Senseval-2 with trained lexicographers Replicability: tests on 1,600 examples of “interest” led to 90%+ replicability

139 • • • • • • • • • • •

References

(Abney 2002) Abney, S.

Bootstrapping.

Proceedings of ACL 2002.

(Blum and Mitchell 1998) Blum, A. and Mitchell, T.

Combining labeled and unlabeled data with co-training

. Proceedings of COLT 1998.

(Chklovski and Mihalcea 2002) Chklovski, T. and Mihalcea, R.

Building a sense tagged corpus with Open Mind Word Expert

. Proceedings of ACL 2002 workshop on WSD.

(Clark, Curran and Osborne 2003) Clark, S. and Curran, J.R. and Osborne, M.

Bootstrapping POS taggers using unlabelled data.

Proceedings of CoNLL 2003.

(Mihalcea 1999) Mihalcea, R.

An automatic method for generating sense tagged corpora

. Proceedings of AAAI 1999.

(Mihalcea 2002) Mihalcea, R.

of LREC 2002.

Bootstrapping large sense tagged corpora

. Proceedings (Mihalcea 2004) Mihalcea, R.

Disambiguation.

Co-training and Self-training for Word Sense

Proceedings of CoNLL 2004.

(Ng and Cardie 2003) Ng, V. and Cardie, C.

learning without redundant views.

Weakly supervised natural language

Proceedings of HLT-NAACL 2003.

(Nigam and Ghani 2000) Nigam, K. and Ghani, R.

applicability of co-training.

Proceedings of CIKM 2000.

Analyzing the effectiveness and

(Sarkar 2001) Sarkar, A.

of NAACL 2001.

Applying cotraining methods to statistical parsing

. Proceedings (Yarowsky 1995) Yarowsky, D.

Unsupervised word sense disambiguation rivaling supervised methods

. Proceedings of ACL 1995.

Part 6: Unsupervised Methods of Word Sense Disambiguation

141

Outline

• • • • • What is Unsupervised Learning?

Task Definition Agglomerative Clustering LSI/LSA Sense Discrimination Using Parallel Texts

142

What is Unsupervised Learning?

• • •

Unsupervised learning identifies patterns in a large sample of data, without the benefit of any manually labeled examples or external knowledge sources These patterns are used to divide the data into clusters, where each member of a cluster has more in common with the other members of its own cluster than any other Note! If you remove manual labels from supervised data and cluster, you may not discover the same classes as in supervised learning – – Supervised Classification identifies features that trigger a sense tag Unsupervised Clustering finds similarity between contexts

143

Day

1 2 3 4

Cluster this Data!

Facts about my day…

Hot Outside?

Slept Well?

Ate Well?

YES YES NO NO NO YES NO NO NO NO NO YES

144

Day

1 2 3 4

Cluster this Data!

Facts about my day…

Hot Outside?

Slept Well?

Ate Well?

YES YES NO NO NO YES NO NO NO NO NO YES

145

Cluster this Data!

Day

1 2 3 4

Hot Outside?

Slept Well?

Ate Well?

YES YES NO NO NO YES NO NO NO NO NO YES

146

Outline

• • • • •

What is Unsupervised Learning?

Task Definition

Agglomerative Clustering LSI/LSA Sense Discrimination Using Parallel Texts

147

Task Definition

• • • •

Unsupervised Word Sense Discrimination:

A class of methods that cluster words based on similarity of context Strong Contextual Hypothesis – – (Miller and Charles, 1991): Words with similar meanings tend to occur in similar contexts (Firth, 1957): “You shall know a word by the company it keeps.” • …words that keep the same company tend to have similar meanings Only use the information available in raw text, do not use outside knowledge sources or manual annotations No knowledge of existing sense inventories, so clusters are not labeled with senses

148

Task Definition

• • •

Resources:

– Large Corpora

Scope:

– – – Typically one targeted word per context to be discriminated Equivalently, measure similarity among contexts Features may be identified in separate “training” data, or in the data to be clustered – Does not assign senses or labels to clusters Word Sense Discrimination reduces to the problem of finding the targeted words that occur in the most similar contexts and placing them in a cluster

149

Outline

• • • • •

What is Unsupervised Learning?

Task Definition

Agglomerative Clustering

LSI/LSA Sense Discrimination Using Parallel Texts

Agglomerative Clustering

(

 

)

) 150

• • • •

Create a similarity matrix of instances to be discriminated – Results in a symmetric “instance by instance” matrix, where each cell contains the similarity score between a pair of instances – Typically a first order representation, where similarity is based on the features observed in the pair of instances Apply Agglomerative Clustering algorithm to matrix – To start, each instance is its own cluster – – Form a cluster from the most similar pair of instances Repeat until the desired number of clusters is obtained Advantages : high quality clustering Disadvantages – computationally expensive, must carry out exhaustive pair wise comparisons

151

Measuring Similarity

• Integer Values – Matching Coefficient – Jaccard Coefficient – Dice Coefficient • Real Values – Cosine



Y X X





2 

X X







 

Y X Y

152

Instances to be Clustered

S1 S2 S3 S4 P-2

adv det det

P-1

det det adj noun

P+1

prep prep verb noun

P+2

det det det noun

fish

Y N Y N

check

N Y N N

river

Y N N N

interest

N Y N N

S1 S2 S3 S4 S1 S2

3 3 4 2 2 0

4 2

2 0 1 1

153

S1 S2 S3 S4 S1

Average Link Clustering aka McQuitty’s Similarity Analysis

3 2 0

S3 4

2 1

2 0 1

S1S3 S2 S4 S1S3 S2 S4

3  2 2  2 .

5 2  2 1  1 .

5 3  2  2 .

5 2 0 2  1  1 .

5 2 0

S1S3S2 S4 S1S3S2 S4

1 .

5  1 .

5  1 .

5 2 1 .

5  1 .

5  1 .

5 2

154

Evaluation of Unsupervised Methods

• • • •

If Sense tagged text is available, can be used for evaluation – But don’t use sense tags for clustering or feature selection!

Assume that sense tags represent “true” clusters, and compare these to discovered clusters – Find mapping of clusters to senses that attains maximum accuracy Pseudo words are especially useful, since it is hard to find data that is discriminated – Pick two words or names from a corpus, and conflate them into one name. Then see how well you can discriminate.

– http://www.d.umn.edu/~kulka020/kanaghaName.html

Baseline Algorithm– group all instances into one cluster, this will reach “accuracy” equal to majority classifier

155

Baseline Performance

C1 C2 C3 Totals S1

0 0 80 80

0 0 35 35

0 0 55 55

Totals

0 0 170 170

C1 C2 C3 Totals S3

0 0 55 55

0 0 35 35

0 0 80 80

Totals

0 0 170 170 (0+0+55)/170 = .32 (0+0+80)/170 = .47

if C3 is S3 if C3 is S1

156

Evaluation

• • • • Suppose that C1 is labeled S1, C2 as S2, and C3 as S3 Accuracy = (10 + 0 + 10) / 170 = 12% Diagonal shows how many members of the cluster actually belong to the sense given on the column Can the “columns” be rearranged to improve the overall accuracy?

– Optimally assign clusters to senses

C1 C2 S1

10 20

C3 Totals

50 80

30 0 5 35

5 40 10 55

Totals

45 60 65 170

157

Evaluation

• • • The assignment of C1 to S2, C2 to S3, and C3 to S1 results in 120/170 = 71% Find the ordering of the columns in the matrix that maximizes the sum of the diagonal. This is an instance of the Assignment Problem from Operations Research, or finding the Maximal Matching of a Bipartite Graph from Graph Theory.

C1 C2 C3 Totals S2

30 0 5 35

5 40 10 55

10 20 50 80

Totals

45 60 65 170

Agglomerative Approach

158

• • • •

(Pedersen and Bruce, 1997) explore discrimination with a small number (approx 30) of features near target word.

– – – – Morphological form of target word (1) Part of Speech two words to left and right of target word (4) Co-occurrences (3) most frequent content words in context Unrestricted collocations (19) most frequent words located one position to left or right of target, OR – Content collocations (19) most frequent content words located one position to left or right of target Features identified in the instances be clustered Similarity measured by matching coefficient Clustered with McQuitty’s Similarity Analysis, Ward’s Method, and the EM Algorithm – Found that McQuitty’s method was the most accurate

Experimental Evaluation

159 • • • Adjectives – – – – Chief, 86% majority (1048) Common, 84% majority (1060) Last, 94% majority (3004) Public, 68% majority (715) Nouns – – – – – Bill, 68% majority (1341) Concern, 64% majority (1235) Drug, 57% majority (1127) Interest, 59% majority (2113) Line, 37% majority (1149) Verbs – – – – Agree, 74% majority (1109) Close, 77% majority (1354) Help, 78% majority (1267) Include, 91% majority (1526) • • • Adjectives – – – – Chief, 86% Common, 80% Last, 79% Public, 63%

Nouns

– – – – –

Bill, 75% Concern, 68% Drug, 65% Interest, 65% Line, 42%

Verbs – – – – Agree, 69% Close, 72% Help, 70% Include, 77%

160

Analysis

• • •

Unsupervised methods may not discover clusters equivalent to the classes learned in supervised learning Evaluation based on assuming that sense tags represent the “true” cluster are likely a bit harsh. Alternatives?

– Humans could look at the members of each cluster and determine the nature of the relationship or meaning that they all share – Use the contents of the cluster to generate a descriptive label that could be inspected by a human First order feature sets may be problematic with smaller amounts of data since these features must occur exactly in the test instances in order to be “matched”

161

Outline

• • • • •

What is Unsupervised Learning?

Task Definition Agglomerative Clustering

LSI/LSA

Sense Discrimination Using Parallel Texts

162

Latent Semantic Indexing/Analysis

• • • • •

Adapted by (Schütze, 1998) to word sense discrimination Represent training data as word co-occurrence matrix Reduce the dimensionality of the co-occurrence matrix via Singular Value Decomposition (SVD) – Significant dimensions are associated with concepts Represent the instances of a target word to be clustered by taking the average of all the vectors associated with all the words in that context – Context represented by an averaged vector Measure the similarity amongst instances via cosine and record in similarity matrix, or cluster the vectors directly

163

Co-occurrence matrix

pc body disk petri lab sales linux debt

apple blood cells ibm data box

2 0 1 0 0 0 2 0 0 3 0 2 0 0 0 0 0 0 0 1 3 0 0 0 1 0 2 0 0 2 1 2 3 0 0 0 2 3 3 3 1 0 3 0 0 0 2 4

tissue graphics memory organ plasma

0 2 0 2 2 0 0 0 0 0 1 0 0 1 1 2 0 0 2 1 2 2 1 0 0 2 0 0 1 0 0 0 0 1 0 1 3 0 0 0

164

Singular Value Decomposition A=UDV’

165

.35

.05

.35

.08

.29

.37

.46

.56

.09

-.49

.13

-.45

-.2

.59

.39

.25

.52

.44

-.60

-.02

-.09

.08

.40

.02

-.09 -.44

.31

.17

.41

.09

-.22

.83

-.68 -.45 -.34 -.31

-.01 -.31

.09

.72

.02

-.21

-.48 -.04

.11

.25

-.08

.30

.24

-.01

.39

-.07 -.49 -.52

.05

.14

.63

-.04

.20

.05

.01

.03

.08

-.3

.20

-.6

-.39

-.26

.43

.31

.08

-.30

-.00

-.02

.00

-.01

-.02

-.00

.00

-.02

-.01

.03

.00

-.07

.08

-.01

-.07

D

166 9.19

6.36

3.99

3.25

2.52

2.30

1.26

0.66

0.00

167

.21

.04

.11

.37

.63

.49

.09

.25

.28

.04

.11

.08

-.37

-.39

.15

-.01

.27

-.51

.11

-.23

-.26

-.47

-.04

.57

-.27

.12

-.45

.50

.20

.15

-.14

.19

-.12

.28

.39

-.32

-.12

.52

-.32

.05

-.12

-.45

.17

-.18

.04

.23

-.30

.39

-.09

-.45

-.05

.02

.64

-.06

-.27

.86

-.04

.06

-.17

-.26

.13

.02

-.32

.17

-.07

.03

-.05

.26

.17

-.13

.08

.02

.29

.05

-.04

-.87

-.18

-.05

-.02

.15

.71

-.06

-.01

.08

-.59

-.32

-.10

.09

-.31

.03

-.41

-.31

.21

.31

-.04

-.62

.31

-.07

.12

-.12

.25

.58

-.12

.08

.12

-.31

-.23

.12

.22

-.58

.03

.44

.07

.03

-.02

-.03

-.71

.07

-.03

-.20

.50

168

Co-occurrence matrix after SVD

apple blood cells ibm data tissue graphics memory organ plasma pc body disk germ lab sales linux debt

.73

.00

.76

.00

.21

.73

.96

1.2

.00

1.2

.00

1.1

1.7

.15

.00

.11 1.3

1.3 .00

.01 1.3

1.2 .00

2.0 .35

.39 1.3

.16 1.7

.00 2.1

2.0

.33

2.1

.49

1.7

2.2

2.7

3.2

.01

1.6

.00

1.5

2.5

.35

.03

.00

.86

.00

.91

.00

.18

.85

1.1

1.5

.77

.85

.72

.86

1.7

.98

1.0

1.1

.00

.84

.00

.77

1.2

.17

.00

.09

1.5

.00

1.4

2.3

.41

.13

.00

169

Effect of SVD

• • •

SVD reduces a matrix to a given number of dimensions This may convert a word level space into a semantic or conceptual space – If “dog” and “collie” and “wolf” are dimensions/columns in the word co-occurrence matrix, after SVD they may be a single dimension that represents “canines” The dimensions are the principle components that may (hopefully) represent the meaning of concepts SVD has effect of smoothing a very sparse matrix, so that there are very few 0 valued cells

170

Context Representation

• •

Represent each instance of the target word to be clustered by averaging the word vectors associated with its context – This creates a “second order” representation of the context The context is represented not only by the words that occur therein, but also the words that occur with the words in the context elsewhere in the training corpus

Second Order Context Representation

• •

I got a new

disk

today!

What do you think of

linux?

171

disk linux apple

.76

.96

blood cells ibm

.00

.01

.16

1.3

1.7

data tissue graphics

2.1

2.7

.00

.03

.91

1.1

memory organ Plasma

.72

1.0

.00

.13

• •

These two contexts share no words in common, yet they are similar!

disk

and

linux

both occur with “Apple”, “IBM”, “data”, “graphics”, and “memory” The two contexts are similar because they share many

second order co-occurrences

172

Second Order Context Representation

•

The bank of the Mississippi River was washed away.

173

First vs. Second Order Representations

• • • •

Comparison made by (Purandare and Pedersen, 2004) Build word co-occurrence matrix using log-likelihood ratio – – – Reduce via SVD Cluster in vector or similarity space Evaluate relative to manually created sense tags Experiments conducted with Senseval-2 data – 24 words, 50-200 training and test examples – Second order representation resulted in significantly better performance than first order, probably due to modest size of data.

Experiments conducted with line, hard, serve – – 4000-5000 instances, divided into 60-40 training-test split First order representation resulted in better performance than second order, probably due to larger amount of data

174

Analysis

• • • •

Agglomerative methods based on direct (first order) features require large amounts of data in order to obtain a reliable set of features Large amounts of data are problematic for agglomerative clustering (due to exhaustive comparisons) Second order representations allow you to make due with smaller amounts of data, and still get a rich (non-sparse) representation of context http://senseclusters.sourceforge.net

is a complete system for performing unsupervised discrimination using first or second order context vectors in similarity or vector space, and includes support for SVD, clustering and evaluation

175

Outline

• • • • •

What is Unsupervised Learning?

Task Definition Agglomerative Clustering LSI/LSA

Sense Discrimination Using Parallel Texts

176

Sense Discrimination Using Parallel Texts

• • • •

There is controversy as to what exactly is a “word sense” (e.g., Kilgarriff, 1997) It is sometimes unclear how fine grained sense distinctions need to be to be useful in practice. Parallel text may present a solution to both problems!

– Text in one language and its translation into another Resnik and Yarowsky (1997) suggest that word sense disambiguation concern itself with sense distinctions that manifest themselves across languages.

– A “bill” in English may be a “pico” (bird jaw) in or a “cuenta” (invoice) in Spanish.

177

Parallel Text

• • •

Parallel Text can be found on the Web and there are several large corpora available (e.g., UN Parallel Text, Canadian Hansards) Manual annotation of sense tags is not required! However, text must be word aligned (translations identified between the two languages). – http://www.cs.unt.edu/~rada/wpt/ Workshop on Parallel Text, NAACL 2003 Given word aligned parallel text, sense distinctions can be discovered. (e.g., Li and and Li, 2002, Diab, 2002)

References

• • • • • • • • • • 178 (Diab, 2002) Diab, Mona and Philip Resnik,

An Unsupervised Method for Word Sense Tagging using Parallel Corpora,

Proceedings of ACL, 2002. (Firth, 1957) A Synopsis of Linguistic Theory 1930-1955. In Studies in Linguistic Analysis, Oxford University Press, Oxford. (Kilgarriff, 1997) “I don’t believe in word senses”, Computers and the Humanities (31) pp. 91-113.

(Li and Li, 2002) Word Translation Disambiguation Using Bilingual Bootstrapping. Proceedings of ACL. Pp. 343-351.

(McQuitty, 1966) Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data. Educational and Psychological Measurement (26) pp. 825-831. (Miller and Charles, 1991) Contextual correlates of semantic similarity. Language and Cognitive Processes, 6 (1) pp. 1 - 28.

(Pedersen and Bruce, 1997) Distinguishing Word Sense in Untagged Text. In Proceedings of EMNLP2. pp 197-207.

(Purandare and Pedersen, 2004) Word Sense Discrimination by Clustering Contexts in Vector and Similarity Spaces. Proceedings of the Conference on Natural Language and Learning. pp. 41-48.

(Resnik and Yarowsky, 1997) A Perspective on Word Sense Disambiguation Methods and their Evaluation. The ACL-SIGLEX Workshop Tagging Text with Lexical Semantics. pp. 79-86. (Schutze, 1998) Automatic Word Sense Discrimination. Computational Linguistics, 24 (1) pp. 97-123.

Part 7: How to Get Started in Word Sense Disambiguation Research

180

Outline

• • • Where to get the required ingredients?

– – Machine Readable Dictionaries Machine Learning Algorithms – – Sense Annotated Data Raw Data Where to get WSD software?

How to get your algorithms tested?

– Senseval

181

Machine Readable Dictionaries

• • •

Machine Readable format (MRD) – – Oxford English Dictionary Collins – Longman Dictionary of Ordinary Contemporary English (LDOCE) Thesauri – add synonymy information – Roget Thesaurus http://www.thesaurus.com

Semantic networks – add more semantic relations – WordNet http://www.cogsci.princeton.edu/~wn/ • Dictionary files, source code – EuroWordNet http://www.illc.uva.nl/EuroWordNet/ • Seven European languages

Machine Learning Algorithms

182

• • • • •

Many implementations available online Weka: Java package of many learning algorithms – – http://www.cs.waikato.ac.nz/ml/weka/ Includes decision trees, decision lists, neural networks, naïve bayes, instance based learning, etc.

C4.5: C implementation of decision trees – http://www.cse.unsw.edu.au/~quinlan/ Timbl: Fast optimized implementation of instance based learning algorithms – http://ilk.kub.nl/software.html

SVM Light: efficient implementation of Support Vector Machines – http://svmlight.joachims.org

Sense Tagged Data

• • •

A lot of annotated data available through Senseval – http://www.senseval.org

Data for lexical sample – – – English (with respect to Hector, WordNet, Wordsmyth) Basque, Catalan, Chinese, Czech, Romanian, Spanish, etc.

Data produced within Open Mind Word Expert project http://teach computers.org

Data for all words – – English, Italian, Czech (Senseval-2 and Senseval-3) SemCor (200,000 running words) http://www.cs.unt.edu/~rada/downloads.html

183

•

Pointers to additional data available from – http://www.senseval.org/data.html

184

Sense Tagged Data – Lexical Sample

The evening ended in a brawl between the different factions in Cubism, but it brought a moment of splendour into the blackouts and bombings of war. [/p] [p] Yet Modigliani was too much a part of the life of Montparnasse, too involved with the individuals leading the " new art " , to remain completely aloof.

In 1914 he had met Hans Arp, the French painter who was to become prominent in the new Dada movement, at the artists' canteen in the Avenue du Maine.

Two years later Arp was living in Zurich, a member of a group of talented emigrant artists who had left their own countries because of the war.

Through casual meetings at cafes, the artists drew together to form a movement in protest against the waste of war, against nationalism and against everything pompous, conventional or boring in the

art

of the Western world.

Sense Tagged Data – SemCor

185

~~The Fulton_County_Grand_Jury said Friday an investigation of Atlanta 's recent primary_election produced …~~

186

Raw Data

• • • •

For use with – – Bootstrapping algorithms Word sense discrimination algorithms British National Corpus – 100 million words covering a variety of genres, styles – http://www.natcorp.ox.ac.uk/ TREC (Text Retrieval Conference) data – – – Los Angeles Times, Wall Street Journal, and more 5 gigabytes of text http://trec.nist.gov/ The Web

187

Outline

•

Where to get the required ingredients?

– – Machine Readable Dictionaries Machine Learning Algorithms – – Sense Annotated Data Raw Data

• • Where to get WSD software?

How to get your algorithms tested?

– Senseval

188

WSD Software – Lexical Sample

• • • • • Duluth Senseval-2 systems – Lexical decision tree systems that participated in Senseval-2 and 3 – http://www.d.umn.edu/~tpederse/senseval2.html

SyntaLex – Enhance Duluth Senseval-2 with syntactic features, participated in Senseval-3 – http://www.d.umn.edu/~tpederse/syntalex.html

WSDShell – Shell for running Weka experiments with wide range of options – http://www.d.umn.edu/~tpederse/wsdshell.html

SenseTools – For easy implementation of supervised WSD, used by the above 3 systems – – Transforms Senseval-formatted data into the files required by Weka http://www.d.umn.edu/~tpederse/sensetools.html

SenseRelate::TargetWord – Identifies the sense of a word based on the semantic relation with its neighbors – – http://search.cpan.org/dist/WordNet-SenseRelate-TargetWord Uses WordNet::Similarity – measures of similarity based on WordNet • http://search.cpan.org/dist/WordNet-Similarity

189

WSD Software – All Words

• •

SenseLearner – – – A minimally supervised approach for all open class words Extension of a system participating in Senseval-3 http://lit.csci.unt.edu/~senselearner – Demo on Sunday, June 26 (1:30-3:30) SenseRelate::AllWords – Identifies the sense of a word based on the semantic relation with its neighbors – – http://search.cpan.org/dist/WordNet-SenseRelate-AllWords Demo on Sunday, June 26 (1:30-3:30)

190

WSD Software – Unsupervised

• • •

Clustering by Committee – http://www.cs.ualberta.ca/~lindek/demos/wordcluster.htm

InfoMap – – Represent the meanings of words in vector space http://infomap-nlp.sourceforge.net

SenseClusters – Finds clusters of words that occur in similar context – – http://senseclusters.sourceforge.net

Demo Sunday, June 26 (4:00-6:00)

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Outline

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Where to get the required ingredients?

– – Machine Readable Dictionaries Machine Learning Algorithms – – Sense Annotated Data Raw Data

•

Where to get WSD software?

• How to get your algorithms tested?

– Senseval

Senseval

• • • • •

Evaluation of WSD systems http://www.senseval.org

Senseval 1: 1999 – about 10 teams Senseval 2: 2001 – about 30 teams Senseval 3: 2004 – about 55 teams Senseval 4: 2007(?) 192

• • •

Provides sense annotated data for many languages, for several tasks – Languages: English, Romanian, Chinese, Basque, Spanish, etc.

– Tasks: Lexical Sample, All words, etc.

Provides evaluation software Provides results of other participating systems

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Senseval

Senseval Evaluations

160 140 120 100 80 60 40 20 0 Senseval 1 Senseval 2 Senseval 3 Tasks Teams Systems

Part 8: Conclusions

195

Outline

• • • • The Web and WSD

Multilingual WSD The Next Five Years (2005-2010) Concluding Remarks

The Web and WSD

196

• • •

The Web has become a source of data for NLP in general, and word sense disambiguation is no exception.

Can find hundreds/thousands(?) of instances of a particular target word just by searching. Search Engines : – Alta Vista – allows scraping, at a modest rate. Insert 5 second delays on your queries to Alta-Vista so as to not overwhelm the system. No API provided, but Perl::LWP works nicely. – http://search.cpan.org/dist/libwww-perl/ Google – does not allow scraping, but provides an API to access search engine. However, the API limits you to 1,000 queries per day. http://www.google.com/apis/

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The Web and WSD

•

The Web can search as a good source of information for selecting or verifying collocations and other kinds of association.

– “strong tea” : 13,000 hits – – “powerful tea” : 428 hits “sparkling tea” : 376 hits

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The Web and WSD

•

You can find sets of related words from the Web. – – http://labs.google.com/sets Give Google Sets two or three words, it will return a set of words it believes are related – Could be the basis of extending features sets for WSD, since many times the words are related in meaning • • Google Sets Input: bank, credit Google Sets Output: bank, credit, stock, full, investment, invoicing, overheads, cash low, administration, produce service, grants, overdue notices – Great source of info about names or current events • • Google Sets Input: Nixon, Carter Google Sets Output: Carter, Nixon, Reagan, Ford, Bush, Eisenhower, Kennedy, Johnson

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A Natural Problem for the Web and WSD

• •

Organize Search Results by concepts, not just names.

– Separate the Irish Republican Army (IRA) from the Individual Retirement Account (IRA).

http://clusty.com is an example of a web site that attempts to cluster content. – Finds a set of pages, and labels them with some descriptive term. – Very similar to problem in word sense discrimination, where cluster is not associated with a known sense.

200

The Web and WSD, not all good news

• • • •

Lots and lots of junk to filter through. Lots of misleading and malicious content on web pages.

Counts as reported by search engines for hits are approximations and vary sometime from query to query. Over time they will change, so it’s very hard to reproduce experimental results over time. Search engines could close down API, prohibit scraping, etc. – there are no promises made. Can be slow to get data from the Web.

201

Outline

• • •

The Web and WSD

• Multilingual WSD

The Next Five Years (2005-2010) Concluding Remarks

202

Multilingual WSD

• •

Parallel text is a potential meeting ground between raw untagged text (like unsupervised methods use) and sense tagged text (like the supervised methods need) A source language word that is translated into various different target language forms may be polysemous in the source language

203

A Clever Way to Sense Tag

• • •

Expertise of native speakers can be used to create sense tagged text, without having to refer to dictionaries! Have a bilingual native speaker pick the proper translation for a word in a given context. http://www.teach-computers.org/word-expert/english-hindi/ http://www.teach-computers.org/word-expert/english-french / This is a much more intuitive way to sense tag text, and depends only on the native speakers expertise, not a set of senses as found in a particular dictionary.

204

Outline

• •

The Web and WSD Multilingual WSD

• • The Next Five Years (2005-2010)

Concluding Remarks

205

The Next Five Years

• • • •

Applications, applications, applications, and applications. Where are the applications? WSD needs to demonstrate an impact on applications in the next five years. Word Sense Disambiguation will be deployed in an increasing number of applications over the next five years.

– However, not in Machine Translation. Too difficult to integrate WSD into current statistical systems, and this won’t change soon.

– Most likely applications include web search tools and email organizers and search tools (like gmail).

If you are writing papers, “bake off” evaluations will meet with more rejection that acceptance If you have a potential application for Word Sense Disambiguation in any of its forms, tell us!! Please!

206

Outline

• •

The Web and WSD Multilingual WSD

•

The Next Five Years (2005-2010)

• Concluding Remarks

207

Concluding Remarks

• • •

Word Sense Disambiguation has something for everyone!

– – – – – – – Statistical Methods Knowledge Based systems Supervised Machine Learning Unsupervised Learning Semi-Supervised Bootstrapping and Co-training Human Annotation of Data The impact of high quality WSD will be huge. NLP consumers have become accustomed to systems that only make coarse grained distinctions between concepts, or who might not make any at all. Real Understanding? Real AI?

208

Thank You!

•

Rada Mihalcea ( [email protected]

) – http://www.cs.unt.edu/~rada

•

Ted Pedersen ( [email protected]

) – http://www.d.umn.edu/~tpederse