EECS 595 / LING 541 / SI 661&761

Natural Language Processing

Fall 2005 Lecture Notes #2

Course logistics

• Instructor: Prof. Dragomir Radev ([email protected]) Ph.D., Computer Science, Columbia University Formerly at IBM TJ Watson Research Center • Times: Thursdays 2:40-5:25 PM, in 411, West Hall • Office hours: TBA, 3080 West Hall Connector Course home page: http://www.si.umich.edu/~radev/NLP-fall2005

Linguistic Fundamentals

Syntactic categories

• Substitution test:

Nathalie likes black

{ }

tabby small

• Open (lexical) and closed (functional) categories:

No-fly-zone yadda yadda yadda the in

Morphology

The dog chased the yellow bird

.

• Parts of speech: eight (or so) general types • Inflection (number, person, tense…) • Derivation (adjective-adverb, noun-verb) • Compounding (separate words or single word) • Part-of-speech tagging • Morphological analysis (prefix, root, suffix, ending)

Part of speech tags

From Church (1991) - 79 tags NN /* singular noun */ IN /* preposition */ AT /* article */ NP /* proper noun */ JJ /* adjective */ , /* comma */ NNS /* plural noun */ CC /* conjunction */ RB /* adverb */ VB /* un-inflected verb */ VBN /* verb +en (taken, looked (passive,perfect)) */ VBD /* verb +ed (took, looked (past tense)) */ CS /* subordinating conjunction */

Jabberwocky (Lewis Carroll)

`Twas brillig, and the slithy toves Did gyre and gimble in the wabe: All mimsy were the borogoves, And the mome raths outgrabe.

"Beware the Jabberwock, my son!

The jaws that bite, the claws that catch!

Beware the Jubjub bird, and shun The frumious Bandersnatch!"

Nouns

• Nouns:

dog, tree, computer, idea

• Nouns vary in number (singular, plural), gender (masculine, feminine, neuter), case (nominative, genitive, accusative, dative) • Latin:

filius

(m),

filia

(f), German:

Mädchen filium

(object) • Clitics

(‘s)

Pronouns

• Pronouns:

she, ourselves, mine

• Pronouns vary in person, gender, number, case (in English: nominative, accusative, possessive, 2nd possessive, reflexive)

Mary saw her in the mirror.

Mary saw herself in the mirror.

• Anaphors:

herself

,

each other

Determiners and adjectives

• Articles:

the, a

• Demonstratives:

this, that

• Adjectives: describe properties • Attributive and predicative adjectives • Agreement: in gender, number • Comparative and superlative (derivative and periphrastic) • Positive form

Verbs

• Actions, activities, and states (

throw, walk, have

) • English: four verb forms • tenses: present, past, future • other inflection: number, person • gerunds and infinitive • aspect: progressive, perfective • voice: active, passive • participles, auxiliaries • irregular verbs • French and Finnish: many more inflections than English

Other parts of speech

• Adverbs, prepositions, particles • phrasal verbs (

the plane took off, take it off

) • particles vs. prepositions (

she ran up a bill/hill

) • Coordinating conjunctions:

and, or, but

• Subordinating conjunctions:

if, because, that, although

• Interjections:

Ouch

!

Phrase structure

• Constraints on word order • Constituents: NP, PP, VP, AP • Phrase structure grammars S NP VP PN V N

Spot chased

Det

a

N

bird

Phrase structure

• Paradigmatic relationships (e.g., constituency) • Syntagmatic relationships (e.g., collocations) S NP VP

That man

VBD NP PP

caught the butterfly

IN

with a

NP

net

Phrase-structure grammars

Peter gave Mary a book.

Mary gave Peter a book.

• Constituent order (SVO, SOV) • imperative forms • sentences with auxiliary verbs • interrogative sentences • declarative sentences • start symbol and rewrite rules • context-free view of language

Sample phrase-structure grammar

S  NP  NP  NP  VP  VP  VP  P  NP VP AT NNS AT NN NP PP VP PP VBD VBD NP IN NP AT  NNS  NNS  NNS  VBD  VBD  VBD  IN  IN  NN 

the children students mountains slept ate saw in of cake

Phrase structure grammars

• Local dependencies • Non-local dependencies • Subject-verb agreement

The women who found the wallet were given a reward.

• wh-extraction

Should Peter buy a book?

Which book should Peter buy?

• Empty nodes

Dependency: arguments and adjuncts

Sue watched the man at the next table.

• Event + dependents (verb arguments are usually NPs) • agent, patient, instrument, goal - semantic roles • subject, direct object, indirect object • transitive, intransitive, and ditransitive verbs • active and passive voice

Subcategorization

• Arguments: subject + complements • adjuncts vs. complements • adjuncts are optional and describe time, place, manner… • subordinate clauses • subcategorization frames

Subcategorization

Subject: The

children

eat candy.

Object: The children eat

candy

.

Prepositional phrase: She put the book

on the table

.

Predicative adjective: We made the man

angry

.

Bare infinitive: She helped me

walk

.

To-infinitive: She likes

to walk

.

Participial phrase: She stopped

singing that tune

at the end.

That-clause: She thinks

that it will rain tomorrow

.

Question-form clauses: She asked me

what book I was reading

.

Subcategorization frames

• Intransitive verbs:

The woman walked

• Transitive verbs:

John loves Mary

• Ditransitive verbs:

Mary gave Peter flowers

• Intransitive with PP:

I rent in Paddington

• Transitive with PP:

She put the book on the table

• Sentential complement:

I know that she likes you

• Transitive with sentential complement:

She told me that Gary is coming on Tuesday

Selectional restrictions and preferences

• Subcategorization frames capture syntactic regularities about complements • Selectional restrictions and preferences capture semantic regularities:

bark

,

eat

Phrase structure ambiguity

• Grammars are used for generating and parsing sentences • Parses • Syntactic ambiguity • Attachment ambiguity:

Our company is training workers.

•

The children ate the cake with a spoon

.

• High vs. low attachment • Garden path sentences:

The horse raced past the barn fell. Is the book on the table red?

Ungrammaticality vs. semantic abnormality

* Slept children the.

# Colorless green ideas sleep furiously.

# The cat barked.

Semantics and pragmatics

• Lexical semantics and compositional semantics • Hypernyms, hyponyms, antonyms, meronyms and holonyms (part-whole relationship, tire is a meronym of car), synonyms, homonyms • Senses of words, polysemous words • Homophony (

bass

).

• Collocations:

white hair

,

white wine

• Idioms:

to kick the bucket

Discourse analysis

• Anaphoric relations:

1. Mary helped Peter get out of the car. He thanked her.

2. Mary helped the other passenger out of the car. The man had asked her for help because of his foot injury.

• Information extraction problems (entity crossreferencing)

Hurricane Hugo destroyed 20,000 Florida homes.

At an estimated cost of one billion dollars, the disaster has been the most costly in the state’s history.

Pragmatics

• The study of how knowledge about the world and language conventions interact with literal meaning.

• Speech acts • Research issues: resolution of anaphoric relations, modeling of speech acts in dialogues

Other areas of NLP

• Linguistics is traditionally divided into phonetics, phonology, morphology, syntax, semantics, and pragmatics.

• Sociolinguistics: interactions of social organization and language.

• Historical linguistics: change over time.

• Linguistic typology • Language acquisition • Psycholinguistics: real-time production and perception of language

Word classes and part-of-speech tagging

Part of speech tagging

• Problems: transport, object, discount, address • More problems: content • French: est, président, fils • “Book that flight” – what is the part of speech associated with “book”?

• POS tagging: assigning parts of speech to words in a text.

• Three main techniques: rule-based tagging, stochastic tagging, transformation-based tagging

Rule-based POS tagging

• Use dictionary or FST to find all possible parts of speech • Use disambiguation rules (e.g., ART+V) • Typically hundreds of constraints can be designed manually

Example in French

^ beginning of sentence La rf b nms u article teneur nfs nms noun feminine singular Moyenne jfs nfs v1s v2s v3s adjective feminine singular en p a b preposition uranium nms noun masculine singular des p r preposition rivi`eres nfp noun feminine plural , x punctuation bien_que cs subordinating conjunction délicate jfs adjective feminine singular À p preposition calculer v verb

Sample rules

BS3 BI1

: A BS3 (3rd person subject personal pronoun) cannot be followed by a BI1 (1st person indirect personal pronoun). In the example: ``il nous faut'' ({\it we need}) - ``il'' has the tag BS3MS and ``nous'' has the tags [BD1P BI1P BJ1P BR1P BS1P]. The negative constraint ``BS3 BI1'' rules out ``BI1P'', and thus leaves only 4 alternatives for the word ``nous''.

N K

: The tag N (noun) cannot be followed by a tag K (interrogative pronoun); an example in the test corpus would be: ``... fleuve qui ...'' (...river, that...). Since ``qui'' can be tagged both as an ``E'' (relative pronoun) and a ``K'' (interrogative pronoun), the ``E'' will be chosen by the tagger since an interrogative pronoun cannot follow a noun (``N'').

R V

:A word tagged with R (article) cannot be followed by a word tagged with V (verb): for example ``l' appelle'' (calls him/her). The word ``appelle'' can only be a verb, but ``l''' can be either an article or a personal pronoun. Thus, the rule will eliminate the article tag, giving preference to the pronoun.

Stochastic POS tagging

• HMM tagger • Pick the most likely tag for this word • P(word|tag) * P(tag|previous n tags) – find tag sequence that maximizes the probability formula • A bigram-based HMM tagger chooses the tag t i word w i for that is most probable given the previous tag t i-1 and the current word w i : • t i = argmax j • t i = argmax j single tag P(t P(t j j |t |t i-1 i-1 ,w i ) )P(w i |t j ) : HMM equation for a

Example

• Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/ADV • People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN • P(VB|TO)P(race|VB) • P(NN|TO)P(race|NN) • TO: to+VB (to sleep), to+NN (to school)

Example (cont’d)

• P(NN|TO) = .021

• P(VB|TO) = .34

• P(race|NN) = .00041

• P(race|VB) = .00003

• P(VB|TO)P(race|VB) = .00001

• P(NN|TO)P(race|NN) = .000007

HMM Tagging

• T = argmax P(T|W), where T=t 1 ,t 2 ,…,t n • By Bayes’ rule: P(T|W) = P(T)P(W|T)/P(W) • Thus we are attempting to choose the sequence of tags that maximizes the rhs of the equation • P(W) can be ignored • P(T)P(W|T) =  P(w i |w 1 t 1 …w i-1 t i 1 t i )P(t i |w 1 t 1 …w i 1 t i-1 )

Transformation-based learning

• P(NN|race) = .98

• P(VB|race) = .02

• Change NN to VB when the previous tag is TO • Types of rules: – The preceding (following) word is tagged z – The word two before (after) is tagged z – One of the two preceding (following) words is tagged z – One of the three preceding (following) words is tagged z – The preceding word is tagged z and the following word is tagged w

Confusion matrix

IN JJ NN IN .2

NNP .2

RB VBD 2.2

VBN JJ .2

8.7

3.3

2.0

.3

2.8

NN 3.3

4.1

.5

.5

NNP RB .7

2.1

1.7

.2

VBD VBN .2

2.6

2.7

.2

4.4

Most confusing: NN vs. NNP vs. JJ, VBD vs. VBN vs. JJ

Readings

• J&M Chapters 1, 2, 3, 8 • “What is Computational Linguistics” by Hans Uszkoreit http://www.coli.uni-sb.de/~hansu/what_is_cl.html

• Lecture notes #1

Readings

• J&M Chapters 3, 8 • Lecture notes #2