Transcript Document 7179126

Named Entity Recognition
http://gate.ac.uk/
http://nlp.shef.ac.uk/
Hamish Cunningham
Kalina Bontcheva
RANLP, Borovets, Bulgaria, 8th September 2003
Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
Information Extraction
• Information Extraction (IE) pulls facts and
structured information from the content of
large text collections.
• IR - IE - NLU
• MUC: Message Understanding
Conferences
• ACE: Automatic Content Extraction
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MUC-7 tasks
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NE: Named Entity recognition and typing
CO: co-reference resolution
TE: Template Elements
TR: Template Relations
ST: Scenario Templates
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An Example
• The shiny red rocket was fired
on Tuesday. It is the brainchild
of Dr. Big Head. Dr. Head is a
staff scientist at We Build
Rockets Inc.
• NE: entities are "rocket",
"Tuesday", "Dr. Head" and
"We Build Rockets"
• CO: "it" refers to the rocket;
"Dr. Head" and "Dr. Big Head"
are the same
• TE: the rocket is "shiny red"
and Head's "brainchild".
• TR: Dr. Head works for We
Build Rockets Inc.
• ST: a rocket launching event
occurred with the
various participants.
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Performance levels
• Vary according to text type, domain,
scenario, language
• NE: up to 97% (tested in English, Spanish,
Japanese, Chinese)
• CO: 60-70% resolution
• TE: 80%
• TR: 75-80%
• ST: 60% (but: human level may be only
80%)
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What are Named Entities?
• NER involves identification of proper
names in texts, and classification into a set
of predefined categories of interest
• Person names
• Organizations (companies, government
organisations, committees, etc)
• Locations (cities, countries, rivers, etc)
• Date and time expressions
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What are Named Entities (2)
• Other common types: measures (percent,
money, weight etc), email addresses, Web
addresses, street addresses, etc.
• Some domain-specific entities: names of drugs,
medical conditions, names of ships,
bibliographic references etc.
• MUC-7 entity definition guidelines (Chinchor’97)
http://www.itl.nist.gov/iaui/894.02/related_projects/muc
/proceedings/ne_task.html
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What are NOT NEs (MUC-7)
• Artefacts – Wall Street Journal
• Common nouns, referring to named entities –
the company, the committee
• Names of groups of people and things named
after people – the Tories, the Nobel prize
• Adjectives derived from names – Bulgarian,
Chinese
• Numbers which are not times, dates,
percentages, and money amounts
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Basic Problems in NE
• Variation of NEs – e.g. John Smith, Mr
Smith, John.
• Ambiguity of NE types: John Smith
(company vs. person)
– May (person vs. month)
– Washington (person vs. location)
– 1945 (date vs. time)
• Ambiguity with common words, e.g. "may"
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More complex problems in NE
• Issues of style, structure, domain, genre etc.
• Punctuation, spelling, spacing, formatting, ...
all have an impact:
Dept. of Computing and Maths
Manchester Metropolitan University
Manchester
United Kingdom
 Tell me more about Leonardo
 Da Vinci
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
12(109)
Applications
• Can help summarisation, ASR and MT
• Intelligent document access
– Browse document collections by the entities that
occur in them
– Formulate more complex queries than IR can
answer
– Application domains:
• News
• Scientific articles, e.g, MEDLINE abstracts
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
14(109)
Some NE Annotated Corpora
• MUC-6 and MUC-7 corpora - English
• CONLL shared task corpora
http://cnts.uia.ac.be/conll2003/ner/ - NEs in English
and German
http://cnts.uia.ac.be/conll2002/ner/ - NEs in Spanish
and Dutch
• TIDES surprise language exercise (NEs in Cebuano
and Hindi)
• ACE – English http://www.ldc.upenn.edu/Projects/ACE/
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The MUC-7 corpus
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100 documents in SGML
News domain
1880 Organizations (46%)
1324 Locations (32%)
887 Persons (22%)
http://www.itl.nist.gov/iaui/894.02/related_proj
ects/muc/proceedings/muc_7_proceedings/mar
sh_slides.pdf
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The MUC-7 Corpus (2)
<ENAMEX TYPE="LOCATION">CAPE CANAVERAL</ENAMEX>,
<ENAMEX TYPE="LOCATION">Fla.</ENAMEX> &MD; Working in
chilly temperatures <TIMEX TYPE="DATE">Wednesday</TIMEX>
<TIMEX TYPE="TIME">night</TIMEX>, <ENAMEX
TYPE="ORGANIZATION">NASA</ENAMEX> ground crews readied
the space shuttle Endeavour for launch on a Japanese satellite retrieval
mission.
<p>
Endeavour, with an international crew of six, was set to blast off from the
<ENAMEX TYPE="ORGANIZATION|LOCATION">Kennedy Space
Center</ENAMEX> on <TIMEX TYPE="DATE">Thursday</TIMEX> at
<TIMEX TYPE="TIME">4:18 a.m. EST</TIMEX>, the start of a 49minute launching period. The <TIMEX TYPE="DATE">nine
day</TIMEX> shuttle flight was to be the 12th launched in darkness.
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NE Annotation Tools - Alembic
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NE Annotation Tools – Alembic (2)
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NE Annotation Tools - GATE
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Corpora and System Development
• Corpora are divided typically into a training and
testing portion
• Rules/Learning algorithms are trained on the training
part
• Tuned on the testing portion in order to optimise
– Rule priorities, rules effectiveness, etc.
– Parameters of the learning algorithm and the features used
• Evaluation set – the best system configuration is run
on this data and the system performance is obtained
• No further tuning once evaluation set is used!
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
22(109)
Performance Evaluation
• Evaluation metric – mathematically defines
how to measure the system’s performance
against a human-annotated, gold standard
• Scoring program – implements the metric and
provides performance measures
– For each document and over the entire corpus
– For each type of NE
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The Evaluation Metric
• Precision = correct answers/answers produced
• Recall = correct answers/total possible correct
answers
• Trade-off between precision and recall
• F-Measure = (β2 + 1)PR / β2R + P
[van Rijsbergen 75]
• β reflects the weighting between precision and
recall, typically β=1
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The Evaluation Metric (2)
• Precision =
Correct + ½ Partially correct
Correct + Incorrect + Partial
• Recall =
Correct + ½ Partially correct
Correct + Missing + Partial
• Why: NE boundaries are often misplaced, so
some partially correct results
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The MUC scorer (1)
Document: 9601020572
----------------------------------------------------------------POS
ACT| COR PAR INC | MIS SPU NON| REC PRE
------------------------+-------------+--------------+----------SUBTASK SCORES
|
|
|
enamex
|
|
|
organization
11
12|
9
0
0|
2
3
0| 82 75
person
24
26| 24
0
0|
0
2
0| 100 92
location
27
31| 25
0
0|
2
6
0| 93 81
…
* * * SUMMARY SCORES * * *
----------------------------------------------------------------POS
ACT| COR PAR INC | MIS SPU NON| REC PRE
-----------------------+-------------+--------------+-----------TASK SCORES
|
|
|
enamex
|
|
|
organizatio 1855 1757|1553
0
37| 265 167
30| 84 88
person
883
859| 797
0
13| 73
49
4| 90 93
location
1322 1406|1199
0
13| 110 194
7| 91 85
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The MUC scorer (2)
• Tracking errors in each document, for
each instance in the text
ENAMEX
ENAMEX
ENAMEX
…
ENAMEX
…
cor inc
cor inc
cor cor
PERSON PERSON "Wernher von Braun" "Braun"
PERSON PERSON "von Braun"
"Braun"
PERSON PERSON "Braun"
"Braun"
cor cor
LOCATI LOCATI "Saturn"
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"Saturn"
The GATE Evaluation Tool
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Regression Testing
• Need to track system’s performance over time
• When a change is made to the system we want
to know what implications are over the entire
corpus
• Why: because an improvement in one case can
lead to problems in others
• GATE offers automated tool to help with the
NE development task over time
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Regression Testing (2)
At corpus level – GATE’s corpus benchmark tool –
tracking system’s performance over time
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
31(109)
Pre-processing for NE Recognition
• Format detection
• Word segmentation (for languages like
Chinese)
• Tokenisation
• Sentence splitting
• POS tagging
32(109)
Two kinds of NE approaches
Knowledge Engineering
Learning Systems
• rule based
• developed by experienced
language engineers
• make use of human intuition
• requires only small amount of
training data
• development could be very
time consuming
• some changes may be hard to
accommodate
• use statistics or other machine
learning
• developers do not need LE
expertise
• requires large amounts of
annotated training data
• some changes may require reannotation of the entire training
corpus
• annotators are cheap (but you
get what you pay for!)
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List lookup approach - baseline
• System that recognises only entities stored in
its lists (gazetteers).
• Advantages - Simple, fast, language
independent, easy to retarget (just create lists)
• Disadvantages - collection and maintenance of
lists, cannot deal with name variants, cannot
resolve ambiguity
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Creating Gazetteer Lists
• Online phone directories and yellow pages for person
and organisation names (e.g. [Paskaleva02])
• Locations lists
– US GEOnet Names Server (GNS) data – 3.9 million locations
with 5.37 million names (e.g., [Manov03])
– UN site: http://unstats.un.org/unsd/citydata
– Global Discovery database from Europa technologies Ltd,
UK (e.g., [Ignat03])
• Automatic collection from annotated training data
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
36(109)
Shallow Parsing Approach
(internal structure)
• Internal evidence – names often have internal structure. These
components can be either stored or guessed, e.g. location:
• Cap. Word + {City, Forest, Center, River}
• e.g. Sherwood Forest
• Cap. Word + {Street, Boulevard, Avenue, Crescent,
Road}
• e.g. Portobello Street
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Problems with the shallow parsing
approach
• Ambiguously capitalised words (first word in sentence)
[All American Bank] vs. All [State Police]
• Semantic ambiguity
"John F. Kennedy" = airport (location)
"Philip Morris" = organisation
• Structural ambiguity
[Cable and Wireless] vs. [Microsoft] and [Dell];
[Center for Computational Linguistics] vs.
message from [City Hospital] for [John Smith]
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Shallow Parsing Approach with
Context
• Use of context-based patterns is helpful in
ambiguous cases
• "David Walton" and "Goldman Sachs" are
indistinguishable
• But with the phrase "David Walton of Goldman
Sachs" and the Person entity "David Walton"
recognised, we can use the pattern "[Person] of
[Organization]" to identify "Goldman Sachs“
correctly.
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Identification of Contextual
Information
• Use KWIC index and concordancer to find
windows of context around entities
• Search for repeated contextual patterns of
either strings, other entities, or both
• Manually post-edit list of patterns, and
incorporate useful patterns into new rules
• Repeat with new entities
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Examples of context patterns
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[PERSON] earns [MONEY]
[PERSON] joined [ORGANIZATION]
[PERSON] left [ORGANIZATION]
[PERSON] joined [ORGANIZATION] as [JOBTITLE]
[ORGANIZATION]'s [JOBTITLE] [PERSON]
[ORGANIZATION] [JOBTITLE] [PERSON]
the [ORGANIZATION] [JOBTITLE]
part of the [ORGANIZATION]
[ORGANIZATION] headquarters in [LOCATION]
price of [ORGANIZATION]
sale of [ORGANIZATION]
investors in [ORGANIZATION]
[ORGANIZATION] is worth [MONEY]
[JOBTITLE] [PERSON]
[PERSON], [JOBTITLE]
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Caveats
• Patterns are only indicators based on
likelihood
• Can set priorities based on frequency
thresholds
• Need training data for each domain
• More semantic information would be useful
(e.g. to cluster groups of verbs)
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Rule-based Example: FACILE
• FACILE - used in MUC-7 [Black et al 98]
• Uses Inxight’s LinguistiX tools for tagging and
morphological analysis
• Database for external information, role similar to a
gazetteer
• Linguistic info per token, encoded as feature vector:
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Text offsets
Orthographic pattern (first/all capitals, mixed, lowercase)
Token and its normalised form
Syntax – category and features
Semantics – from database or morphological analysis
Morphological analyses
• Example:
(1192 1196 10 T C "Mrs." "mrs." (PROP TITLE) (ˆPER_CIV_F)
(("Mrs." "Title" "Abbr")) NIL)
PER_CIV_F – female civilian (from database)
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FACILE (2)
• Context-sensitive rules written in special rule notation,
executed by an interpreter
• Writing rules in PERL is too error-prone and hard
• Rules of the kind:
A => B\C/D, where:
– A is a set of attribute-value expressions and optional score, the
attributes refer to elements of the input token feature vector
– B and D are left and right context respectively and can be empty
– B, C, D are sequences of attribute-value pairs and Klene regular
expression operations; variables are also supported
• [syn=NP, sem=ORG] (0.9) =>
\ [norm="university"],
[token="of"],
[sem=REGION|COUNTRY|CITY] / ;
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FACILE (3)
# Rule for the mark up of person names when the first name is not
# present or known from the gazetteers: e.g 'Mr J. Cass',
[SYN=PROP,SEM=PER, FIRST=_F, INITIALS=_I, MIDDLE=_M,
LAST=_S] #_F, _I, _M, _S are variables, transfer info from RHS
=>
[SEM=TITLE_MIL|TITLE_FEMALE|TITLE_MALE]
\[SYN=NAME, ORTH=I|O, TOKEN=_I]?,
[ORTH=C|A, SYN=PROP, TOKEN=_F]?,
[SYN=NAME, ORTH=I|O, TOKEN=_I]?,
[SYN=NAME, TOKEN=_M]?,
[ORTH=C|A|O,SYN=PROP,TOKEN=_S, SOURCE!=RULE]
#proper name, not recognised by a rule
/;
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FACILE (4)
• Preference mechanism:
– The rule with the highest score is preferred
– Longer matches are preferred to shorter matches
– Results are always one semantic categorisation of
the named entity in the text
• Evaluation (MUC-7 scores):
–
–
–
–
Organization: 86% precision, 66% recall
Person: 90% precision, 88% recall
Location: 81% precision, 80% recall
Dates: 93% precision, 86% recall
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Example Rule-based System - ANNIE
• Created as part of GATE
• GATE – Sheffield’s open-source infrastructure
for language processing
• GATE automatically deals with document
formats, saving of results, evaluation, and
visualisation of results for debugging
• GATE has a finite-state pattern-action rule
language, used by ANNIE
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NE Components
The ANNIE system – a reusable and easily extendable set of
components
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Gazetteer lists for rule-based NE
• Needed to store the indicator strings for the
internal structure and context rules
• Internal location indicators – e.g., {river,
mountain, forest} for natural locations; {street,
road, crescent, place, square, …}for address
locations
• Internal organisation indicators – e.g.,
company designators {GmbH, Ltd, Inc, …}
• Produces Lookup results of the given kind
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The Named Entity Grammars
• Phases run sequentially and constitute a cascade of
FSTs over the pre-processing results
• Hand-coded rules applied to annotations to identify
NEs
• Annotations from format analysis, tokeniser, sentence
splitter, POS tagger, and gazetteer modules
• Use of contextual information
• Finds person names, locations, organisations, dates,
addresses.
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NE Rule in JAPE
JAPE: a Java Annotation Patterns Engine
• Light, robust regular-expression-based processing
• Cascaded finite state transduction
• Low-overhead development of new components
• Simplifies multi-phase regex processing
Rule: Company1
Priority: 25
(
( {Token.orthography == upperInitial} )+ //from tokeniser
{Lookup.kind == companyDesignator} //from gazetteer lists
):match
-->
:match.NamedEntity = { kind=company, rule=“Company1” }
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Named Entities in GATE
Using co-reference to classify
ambiguous NEs
• Orthographic co-reference module that matches
proper names in a document
• Improves NE results by assigning entity type to
previously unclassified names, based on
relations with classified NEs
• May not reclassify already classified entities
• Classification of unknown entities very useful for
surnames which match a full name, or abbreviations,
e.g. [Bonfield] will match [Sir Peter Bonfield];
[International Business Machines Ltd.] will
match [IBM]
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Named Entity Coreference
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Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
55(109)
Machine Learning Approaches
• ML approaches frequently break down the NE task in
two parts:
– Recognising the entity boundaries
– Classifying the entities in the NE categories
• Some work is only on one task or the other
• Tokens in text are often coded with the IOB scheme
– O – outside, B-XXX – first word in NE, I-XXX – all other
words in NE
– Easy to convert to/from inline MUC-style markup
– Argentina
B-LOC
played
O
with
O
Del
B-PER
Bosque
I-PER
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IdentiFinder [Bikel et al 99]
• Based on Hidden Markov Models
• Their HMM has 7 regions – one for each MUC type,
not-name, begin-sentence and end-sentence
• Features
–
–
–
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Capitalisation
Numeric symbols
Punctuation marks
Position in the sentence
14 features in total, combining above info, e.g.,
containsDigitAndDash (09-96), containsDigitAndComma
(23,000.00)
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IdentiFinder (2)
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Back-off models and smoothing
Unknown words
Further back-off and smoothing
Different strategies for name-class bigrams,
first-word bigrams and non-first-word bigrams
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IdentiFinder (3)
• MUC-6 (English) and MET-1(Spanish) corpora used for
evaluation
• Mixed case English
– IdentiFinder - 94.9% f-measure
– Best rule-based – 96.4%
• Spanish mixed case
– IdentiFinder – 90%
– Best rule-based - 93%
– Lower case names, noisy training data, less training data
• Training data: 650,000 words, but similar performance
with half of the data. Less than 100,000 words reduce
the performance to below 90% on English
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MENE [Borthwick et al 98]
• Combining rule-based and ML NE to achieve
better performance
• Tokens tagged as: XXX_start, XXX_continue,
XXX_end, XXX_unique, other (non-NE),
where XXX is an NE category
• Uses Maximum Entropy
– One only needs to find the best features for the
problem
– ME estimation routine finds the best relative
weights for the features
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MENE (2)
• Features
– Binary features – “token begins with capitalised
letter”, “token is a four-digit number”
– Lexical features – dependencies on the
surrounding tokens (window ±2) e.g., “Mr” for
people, “to” for locations
– Dictionary features – equivalent to gazetteers (first
names, company names, dates, abbreviations)
– External systems – whether the current token is
recognised as an NE by a rule-based system
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MENE (3)
• MUC-7 formal run corpus
– MENE – 84.2% f-measure
– Rule-based systems it uses – 86% - 91 %
– MENE + rule-based systems – 92%
• Learning curve
–
–
–
–
20 docs – 80.97%
40 docs – 84.14%
100 docs – 89.17%
425 docs – 92.94%
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Fine-grained Classification of NEs
[Fleischman 02]
• Finer-grained categorisation needed for applications like
question answering
• Person classification into 8 sub-categories – athlete,
politician/government, clergy, businessperson,
entertainer/artist, lawyer, doctor/scientist, police.
• Supervised approach using local context and global
semantic information such as WordNet
• Used a decision list classifier and Identifinder to
construct automatically training set from untagged data
• Held-out set of 1300 instances hand annotated
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Fine-grained Classification of NEs (2)
• Word frequency features
– For each 8 categories 10 distinct word positions = 80
features per instance
– 3 words before & after the instance
– The two-word bigrams immediately before and after the
instance
– The three-word trigrams before/after the instance
• Topic signatures and WordNet information
– Compute lists of terms that signal relevance to a
topic/category [Lin&Hovy 00] & expand with WordNet
synonyms to counter unseen examples
– Politician – campaign, republican, budget
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Fine-grained Classification of NEs (3)
• Due to differing contexts, instances of the same name
in a single text were classified differently
• MemRun chooses the prevailing sub-category based on
their most frequent classification
• Othomatching-like algorithm is developed to match
George Bush, Bush, and George W. Bush
• Expts with k-NN, Naïve Bayes, SVMs, Neural
Networks and C4.5 show that C4.5 is best
• Future work: treating finer grained classification as a
WSD task (categories are different senses of a person)
65(109)
Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
66(109)
Multilingual Named Entity
Recognition
• Recent experiments are aimed at NE
recognition in multiple languages
• TIDES surprise language evaluation exercise
measures how quickly researchers can develop
NLP components in a new language
• CONLL’02, CONLL’03 focus on languageindependent NE recognition
67(109)
Analysis of the NE Task in Multiple
Languages [Palmer&Day 97]
Language
NE
4454
Time/
Date
17.2%
Numeric
exprs.
1.8%
Org/Per/
Loc
80.9%
Chinese
English
2242
10.7%
9.5%
79.8%
French
2321
18.6%
3%
78.4%
Japanese
2146
26.4%
4%
69.6%
Portuguese 3839
17.7%
12.1%
70.3%
Spanish
24.6%
3%
72.5%
3579
68(109)
Analysis of Multilingual NE (2)
• Numerical and time expressions are very easy
to capture using rules
• Constitute together about 20-30% of all NEs
• All numerical expressions in the 6 languages
required only 5 patterns
• Time expressions similarly require only a few
rules (less than 30 per language)
• Many of these rules are reusable across the
languages
69(109)
Analysis of Multilingual NE (3)
• Suggest a method for calculating the lower bound for
system performance given a corpus in the target
language
• Conclusion: Much of the NE task can be achieved by
simple string analysis and common phrasal contexts
• Zipf’s law: the prevalence of frequent phenomena
allow high scores to be achieved directly from the
training data
• Chinese, Japanese, and Portuguese corpora had a
lower bound above 70%
• Substantial further advances require language
specificity
70(109)
What is needed for multilingual NE
• Extensive support for non-Latin scripts and
text encodings, including conversion utilities
– Automatic recognition of encoding [Ignat et al03]
– Occupied up to 2/3 of the TIDES Hindi effort
• Bi-lingual dictionaries
• Annotated corpus for evaluation
• Internet resources for gazetteer list collection
(e.g., phone books, yellow pages, bi-lingual
pages)
71(109)
Multilingual support - Alembic
Japanese
example
72(109)
Editing Multilingual Data
GATE Unicode Kit (GUK)
Complements Java’s facilities
• Support for defining
Input Methods (IMs)
• currently 30 IMs
for 17 languages
• Pluggable in other
applications (e.g.
JEdit)
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Multilingual Data - GATE
All processing, visualisation and editing tools use GUK
74(109)
Gazetteer-based Approach to
Multilingual NE [Ignat et al 03]
• Deals with locations only
• Even more ambiguity than in one language:
– Multiple places that share the same name, such as
the fourteen cities and villages in the world called
‘Paris’
– Place names that are also words in one or more
languages, such as ‘And’ (Iran), ‘Split’ (Croatia)
– Places have varying names in different languages
(Italian ‘Venezia’ vs. English ‘Venice’, German
‘Venedig’, French ‘Venise’)
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Gazetteer-based multilingual NE (2)
• Disambiguation module applies heuristics
based on location size and country mentions
(prefer the locations from the country
mentioned most)
• Performance evaluation:
– 853 locations from 80 English texts
– 96.8% precision
– 96.5% recall
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Machine Learning for Multilingual NE
• CONLL’2002 and 2003 shared tasks were NE
in Spanish, Dutch, English, and German
• The most popular ML techniques used:
– Maximum Entropy (5 systems)
– Hidden Markov Models (4 systems)
– Connectionist methods (4 systems)
• Combining ML methods has been shown to
boost results
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ML for NE at CONLL (2)
• The choice of features is at least as important as
the choice of ML algorithm
–
–
–
–
–
Lexical features (words)
Part-of-speech
Orthographic information
Affixes
Gazetteers
• External, unmarked data is useful to derive
gazetteers and for extracting training instances
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ML for NE at CONLL (3)
• English (f-measure)
– Baseline 59.5%
– Systems – between 60.2% and 88.76%
• German (f-measure)
– Baseline – 30.3%
– Systems – between 47.7% and 72.4%
• Spanish (f-measure)
– Baseline – 35.9%
– Systems – between 60.9% and 81.4%
• Dutch (f-measure)
– Baseline – 53.1%
– Systems – between 56.4% and 77%
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Language Independent NE Recognition
[Cucerzan&Yarowsky 02]
• Uses iterative learning and re-estimation of
contextual and morphological patterns,
using tri models
• Learns from unannotated text and requires
only small list of labelled names, without
using other language-specific tools
• Word internal features:
– Some prefixes and suffixes are good indicators
– For example -escu, -wski, -ova, -ov for person
names
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Language Independent NE Recognition
[Cucerzan&Yarowsky 02]
• Classify all occurrences of an entity of the
text together by combining the contextual
and morphological clues from each instance
• “One NE class per document/discourse”
assumption similar to the “one sense per
discourse” assumption used in word sense
disambiguation [Gale,Church&Yarowsky 92]
• 70.5% - 75.4% f-measure for Romanian
• Measured on two tasks – NE identification and
classification (NE boundaries are pre-defined)
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TIDES surprise language exercise
• Collaborative effort between a number of sites
to develop resources and tools for various LE
tasks on a surprise language
• Tasks: IE (including NE), machine translation,
summarisation, cross-language IR
• Dry-run lasted 10 days on the Cebuano
language from the Philippines
• Surprise language was Hindi, announced at the
start of June 2003; duration 1 month
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Language categorisation
• LDC – survey of 300 largest languages (by
population) to establish what resources are
available
• http://www.ldc.upenn.edu/Projects/TIDES/lan
guage-summary-table.html
• Classification dimensions:
– Dictionaries, news texts, parallel texts, e.g., Bible
– Script, orthography, words separated by spaces
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The Surprise Languages
• Cebuano:
– Latin script and words are spaced, but
– Few resources and little work, so
– Medium difficulty
• Hindi
– Non-latin script, different encodings used, words
are spaced, no capitalisation
– Many resources available
– Medium difficulty
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Named Entity Recognition for TIDES
• Information on other systems and results from
TIDES is still unavailable to non-TIDES
participants
• Will be made available by the end of 2003 in a
Special issue of ACM Transactions on Asian
Language Information Processing (TALIP).
Rapid Development of Language Capabilities:
The Surprise Languages
• The Sheffield approach is presented below,
because it is not subject to these restrictions
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Dictionary-based Adaptation of
an English POS tagger
• Substituted Hindi/Cebuano lexicon for English
one in a Brill-like tagger
• Hindi/Cebuano lexicon derived from a bi-lingual
dictionary
• Used empty ruleset since no training data
available
• Used default heuristics (e.g. return NNP for
capitalised words)
• Very experimental, but reasonable results
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Evaluation of the Tagger
• No formal evaluation was possible
• Estimate around 67% accuracy on Hindi –
evaluated by a native speaker on 1000 words
• Created in 2 person days
• Results and a tagging service made available
to other researchers in TIDES
• Important pre-requisite for NE recognition
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NE grammars
• Most English JAPE rules based on POS tags
and gazetteer lookup
• Grammars can be reused for languages with
similar word order, orthography etc.
• No time to make detailed study of Cebuano,
but very similar in structure to English
• Most of the rules left as for English, but some
adjustments to handle especially dates
• Used both English and Cebuano grammars
and gazetteers, because NEs appear in both
languages
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89(109)
Evaluation Results
Cebuano
Entity
P
R
F
English
Baseline
P
Person
71
65
68
36
36
36
Org
75
71
73
31
47
38
Location
73
78
76
65
7
12
Date
83
100
92
42
58
49
Total
76
79
77.5 45
41.7
43
90(109)
R
F
Structure of the Tutorial
•
•
•
•
•
task definition
applications
corpora, annotation
evaluation and testing
how to
– preprocessing
– approaches to NE
– baseline
– rule-based approaches
– learning-based approaches
• multilinguality
• future challenges
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Future challenges
• Towards semantic tagging of entities
• New evaluation metrics for semantic entity
recognition
• Expanding the set of entities recognised – e.g.,
vehicles, weapons, substances (food, drug)
• Finer-grained hierarchies, e.g., types of
Organizations (government, commercial,
educational, etc.), Locations (regions,
countries, cities, water, etc)
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Future challenges (2)
• Standardisation of the annotation formats
– [Ide & Romary 02] – RDF-based annotation
standards
– [Collier et al 02] – multi-lingual named entity
annotation guidelines
– Aimed at defining how to annotate in order to
make corpora more reusable and lower the
overhead of writing format conversion tools
• MUC used inline markup
• TIDES and ACE used stand-off markup, but two
different kinds (XML vs one-word per line)
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Towards Semantic Tagging of Entities
• The MUC NE task tagged selected segments of text
whenever that text represents the name of an entity.
• In ACE (Automated Content Extraction), these names
are viewed as mentions of the underlying entities. The
main task is to detect (or infer) the mentions in the text
of the entities themselves.
• ACE focuses on domain- and genre-independent
approaches
• ACE corpus contains newswire, broadcast news (ASR
output and cleaned), and newspaper reports (OCR
output and cleaned)
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ACE Entities
• Dealing with
– Proper names – e.g., England, Mr. Smith, IBM
– Pronouns – e.g., he, she, it
– Nominal mentions – the company, the spokesman
• Identify which mentions in the text refer to
which entities, e.g.,
– Tony Blair, Mr. Blair, he, the prime minister, he
– Gordon Brown, he, Mr. Brown, the chancellor
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ACE Example
<entity ID="ft-airlines-27-jul-2001-2"
GENERIC="FALSE"
entity_type = "ORGANIZATION">
<entity_mention ID="M003"
TYPE = "NAME"
string = "National Air Traffic Services">
</entity_mention>
<entity_mention ID="M004"
TYPE = "NAME"
string = "NATS">
</entity_mention>
<entity_mention ID="M005"
TYPE = "PRO"
string = "its">
</entity_mention>
<entity_mention ID="M006"
TYPE = "NAME"
string = "Nats">
</entity_mention>
</entity>
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ACE Entities (2)
• Some entities can have different roles, i.e.,
behave as Organizations, Locations, or Persons
– GPEs (Geo-political entities)
• New York [GPE – role: Person], flush with Wall
Street money, has a lot of loose change jangling
in its pockets.
• All three New York [GPE – role: Location]
regional commuter train systems were found to
be punctual more than 90 percent of the time.
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Further information on ACE
• ACE is a closed-evaluation initiative, which
does not allow the publication of results
• Further information on guidelines and corpora
is available at:
• http://www.ldc.upenn.edu/Projects/ACE/
• ACE also includes other IE tasks, for further
details see Doug Appelt’s presentation:
http://www.clsp.jhu.edu/ws03/groups/sparse/pr
esentations/doug.ppt
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Evaluating Richer NE Tagging
• Need for new metrics
when evaluating
hierarchy/ontologybased NE tagging
• Need to take into
account distance in the
hierarchy
• Tagging a company as a
charity is less wrong
than tagging it as a
person
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THANK YOU!
100(109)
Further Reading
•
•
•
•
•
•
•
•
Aberdeen J., Day D., Hirschman L., Robinson P. and Vilain M. 1995. MITRE:
Description of the Alembic System Used for MUC-6. MUC-6 proceedings.
Pages141-155. Columbia, Maryland. 1995.
Black W.J., Rinaldi F., Mowatt D. Facile: Description of the NE System Used For
MUC-7. Proceedings of 7th Message Understanding Conference, Fairfax, VA, 19
April - 1 May, 1998.
Borthwick. A. A Maximum Entropy Approach to Named Entity Recognition.
PhD Dissertation. 1999
Bikel D., Schwarta R., Weischedel. R. An algorithm that learns what’s in a name.
Machine Learning 34, pp.211-231, 1999
Carreras X., Màrquez L., Padró. 2002. Named Entity Extraction using AdaBoost.
The 6th Conference on Natural Language Learning. 2002
Chang J.S., Chen S. D., Zheng Y., Liu X. Z., and Ke S. J. Large-corpus-based
methods for Chinese personal name recognition. Journal of Chinese Information
Processing, 6(3):7-15, 1992
Chen H.H., Ding Y.W., Tsai S.C. and Bian G.W. Description of the NTU System
Used for MET2. Proceedings of 7th Message Understanding Conference, Fairfax,
VA, 19 April - 1 May, 1998.
Chinchor. N. MUC-7 Named Entity Task Definition Version 3.5.
Available by from ftp.muc.saic.com/pub/MUC/MUC7-guidelines, 1997
101(109)
Further reading (2)
•
•
•
•
•
•
•
•
Collins M., Singer Y. Unsupervised models for named entity classification
In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural
Language Processing and Very Large Corpora, 1999
Collins M. Ranking Algorithms for Named-Entity Extraction: Boosting and the Voted
Perceptron. Proceedings of the 40th Annual Meeting of the ACL, Philadelphia, pp.
489-496, July 2002 Gotoh Y., Renals S. Information extraction from broadcast news,
Philosophical Transactions of the Royal Society of London, series A: Mathematical,
Physical and Engineering Sciences, 2000.
Grishman R. The NYU System for MUC-6 or Where's the Syntax? Proceedings of the
MUC-6 workshop, Washington. November 1995.
[Ign03a] C. Ignat and B. Pouliquen and A. Ribeiro and R. Steinberger. Extending and
Information Extraction Tool Set to Eastern-European Languages. Proceedings of
Workshop on Information Extraction for Slavonic and other Central and Eastern
European Languages (IESL'03). 2003.
Krupka G. R., Hausman K. IsoQuest Inc.: Description of the NetOwlTM Extractor
System as Used for MUC-7. Proceedings of 7th Message Understanding
Conference, Fairfax, VA, 19 April - 1 May, 1998.
McDonald D. Internal and External Evidence in the Identification and Semantic
Categorization of Proper Names. In B.Boguraev and J. Pustejovsky editors: Corpus
Processing for Lexical Acquisition. Pages21-39. MIT Press. Cambridge, MA. 1996
Mikheev A., Grover C. and Moens M. Description of the LTG System Used for MUC7. Proceedings of 7th Message Understanding Conference, Fairfax, VA, 19 April - 1
May, 1998
Miller S., Crystal M., et al. BBN: Description of the SIFT System as Used for MUC-7.
Proceedings of 7th Message Understanding Conference, Fairfax, VA, 19 April - 1
May, 1998
102(109)
Further reading (3)
•
•
•
•
•
•
•
Palmer D., Day D.S. A Statistical Profile of the Named Entity Task.
Proceedings of the Fifth Conference on Applied Natural Language
Processing, Washington, D.C., March 31- April 3, 1997.
Sekine S., Grishman R. and Shinou H. A decision tree method for finding
and classifying names in Japanese texts. Proceedings of the Sixth
Workshop on Very Large Corpora, Montreal, Canada, 1998
Sun J., Gao J.F., Zhang L., Zhou M., Huang C.N. Chinese Named Entity
Identification Using Class-based Language Model. In proceeding of the
19th International Conference on Computational Linguistics
(COLING2002), pp.967-973, 2002.
Takeuchi K., Collier N. Use of Support Vector Machines in Extended
Named Entity Recognition. The 6th Conference on Natural Language
Learning. 2002
D.Maynard, K. Bontcheva and H. Cunningham. Towards a semantic
extraction of named entities. Recent Advances in Natural Language
Processing, Bulgaria, 2003.
M. M. Wood and S. J. Lydon and V. Tablan and D. Maynard and H.
Cunningham. Using parallel texts to improve recall in IE. Recent Advances
in Natural Language Processing, Bulgaria, 2003.
D.Maynard, V. Tablan and H. Cunningham. NE recognition without training
data on a language you don't speak. ACL Workshop on Multilingual and
Mixed-language Named Entity Recognition: Combining Statistical and
Symbolic Models, Sapporo, Japan, 2003.
103(109)
Further reading (4)
•
•
•
•
•
•
•
H. Saggion, H. Cunningham, K. Bontcheva, D. Maynard, O. Hamza, Y. Wilks.
Multimedia Indexing through Multisource and Multilingual Information
Extraction; the MUMIS project. Data and Knowledge Engineering, 2003.
D. Manov and A. Kiryakov and B. Popov and K. Bontcheva and D. Maynard, H.
Cunningham. Experiments with geographic knowledge for information
extraction. Workshop on Analysis of Geographic References, HLT/NAACL'03,
Canada, 2003.
H. Cunningham, D. Maynard, K. Bontcheva, V. Tablan. GATE: A Framework
and Graphical Development Environment for Robust NLP Tools and
Applications. Proceedings of the 40th Anniversary Meeting of the Association
for Computational Linguistics (ACL'02). Philadelphia, July 2002.
H. Cunningham. GATE, a General Architecture for Text Engineering.
Computers and the Humanities, volume 36, pp. 223-254, 2002.
D. Maynard, H. Cunningham, K. Bontcheva, M. Dimitrov. Adapting A Robust
Multi-Genre NE System for Automatic Content Extraction. Proc. of the 10th
International Conference on Artificial Intelligence: Methodology, Systems,
Applications (AIMSA 2002), 2002.
E. Paskaleva and G. Angelova and M.Yankova and K. Bontcheva and H.
Cunningham and Y. Wilks. Slavonic Named Entities in GATE. 2003. CS-0201.
K. Pastra, D. Maynard, H. Cunningham, O. Hamza, Y. Wilks. How feasible is
the reuse of grammars for Named Entity Recognition? Language Resources
and Evaluation Conference (LREC'2002), 2002.
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