Named Entity Recognition

Named Entity Recognition
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 3(110)

MUC-7 tasks NE: Named Entity recognition and typing
CO: co-reference resolution TE: Template Elements TR: Template Relations ST: Scenario Templates 4(110)

An Example 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. 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. 5(110)

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%) 6(110)

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 7(110)

What are Named Entities (2)
Other common types: measures (percent, money, weight etc), 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] 8(110)

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 9(110)

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" 10(110)

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 11(110)

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(110)

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 Example application domains: News Scientific articles, e.g, MEDLINE abstracts 13(110)

Application -Threat tracker
Search by entity: 14(110)

Application Example - KIM
Browsing by entity and ontology: 15(110)

Application Example - KIM
Ontotext’s KIM formal query over OWL (including relations between entities) and results 16(110)

Application Example - Perseus
Time-line and geographic visualisation: 17(110)

Some NE Annotated Corpora
MUC-6 and MUC-7 corpora - English CONLL shared task corpora - NEs in English and German - NEs in Spanish and Dutch TIDES surprise language exercise (NEs in Cebuano and Hindi) ACE – English - 19(110)

The MUC-7 corpus 100 documents in SGML News domain
1880 Organizations (46%) 1324 Locations (32%) 887 Persons (22%) Inter-annotator agreement very high (~97%) 20(110)

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 49-minute launching period. The <TIMEX TYPE="DATE">nine day</TIMEX> shuttle flight was to be the 12th launched in darkness. 21(110)

NE Annotation Tools - Alembic
22(110)

NE Annotation Tools – Alembic (2)
23(110)

NE Annotation Tools - GATE
24(110)

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! 25(110)

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 27(110)

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 28(110)

The Evaluation Metric (2)
We may also want to take account of partially correct answers: 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 29(110)

The MUC scorer (2) Using the detailed report we can track errors in each document, for each NE in the text ENAMEX cor inc PERSON PERSON "Wernher von Braun" "Braun" ENAMEX cor inc PERSON PERSON "von Braun" "Braun" ENAMEX cor cor PERSON PERSON "Braun" "Braun" … ENAMEX cor cor LOCATI LOCATI "Saturn" "Saturn" 31(110)

The GATE Evaluation Tool
32(110)

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 33(110)

Regression Testing (2) At corpus level – GATE’s corpus benchmark tool – tracking system’s performance over time 34(110)

Pre-processing for NE Recognition
Format detection Word segmentation (for languages like Chinese) Tokenisation Sentence splitting POS tagging 36(110)

Two kinds of NE approaches
Knowledge Engineering 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 Learning Systems use statistics or other machine learning developers do not need LE expertise requires large amounts of annotated training data some changes may require re-annotation of the entire training corpus annotators are cheap (but you get what you pay for!) 37(110)

Baseline: list lookup approach
System that recognises only entities stored in its lists (gazetteers). Advantages - Simple, fast, language independent, easy to retarget (just create lists) Disadvantages – impossible to enumerate all names, collection and maintenance of lists, cannot deal with name variants, cannot resolve ambiguity 38(110)

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: Global Discovery database from Europa technologies Ltd, UK (e.g., [Ignat03]) Automatic collection from annotated training data 39(110)

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 41(110)

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] 42(110)

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. 43(110)

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 44(110)

Examples of context patterns
[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] 45(110)

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) 46(110)

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: 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: ( T C "Mrs." "mrs." (PROP TITLE) (ˆPER_CIV_F) (("Mrs." "Title" "Abbr")) NIL) PER_CIV_F – female civilian (from database) 47(110)

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] / ; 48(110)

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 /; Starting our explanation bottom-up, the rule looks for a token with upper initial or all capitalised or mixed capital and lowercase letters (e.g McDonald) which has been recognised as such by the tagger, and which has not been tagged semantically by another rule. The token may be preceded by a token that the tagger knows is a name, or by an initial or a first name, or another initial. Any combination of these will form part of the sequence to be annotated. What is obligatory for the rule to work is that a 'title' word has to precede the whole sequence. Note the variable unification, that binds the value to be assigned to the TOKEN attributes of the sequence that will be identified to specific attributes in the resulting annotation (e.g TOKEN=\_S will denote the LAST name of the person); this information will be available for use in more advanced stages e.g template filling. 49(110)

FACILE (4) Preference mechanism: Evaluation (MUC-7 scores):
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 50(110)

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 ANNIE modified for MUC guidelines – 89.5% f-measure on MUC-7 corpus 51(110)

NE Components The ANNIE system – a reusable and easily extendable set of components 52(110)

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 53(110)

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. 54(110)

JAPE: a Java Annotation Patterns Engine
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” } 55(110)

Named Entities in GATE 56(110)

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] 57(110)

Named Entity Coreference
58(110)

DEMO 59(110)

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 61(110)

IdentiFinder [Bikel et al 99]
Based on Hidden Markov Models Features 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) 62(110)

IdentiFinder (2) MUC-6 (English) and MET-1(Spanish) corpora used for evaluation Mixed case English IdentiFinder % 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 63(110)

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 64(110)

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 65(110)

MENE (3) MUC-7 formal run corpus Learning curve 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% 66(110)

NE Recognition without Gazetteers [Mikheev et al 99]
How big should gazetteer lists be? Experiment with simple list lookup approach on MUC-7 corpus Learned lists – MUC-7 training corpus 1228 person names 809 organisations 770 locations Common lists (from the Web) 5000 locations 33,000 organisations 27,000 person names 67(110)

NE Recognition without Gazetteers (2)
Category Learned Common Combined Recall Precision Preci-sion ORG 49 75 3 51 50 72 PER 26 92 31 81 47 85 LOC 76 93 74 94 86 90 68(110)

System combines rule-based grammars and statistical (MaxEnt) models Full gaz – 4900 LOC, 30,000 ORG, 10,000 PER Some locs – 200 countries + continents + 8 planets Ltd gaz – Some locs + lists inferred from 30 processed texts in the same domain Full Gaz Ltd Gaz Some locs No Gaz rec prec ORG 90 93 87 89 86 85 PER 96 98 92 97 95 LOC 94 91 46 59 The problem is that the most high-frequency names, esp locations do not have contextual clues by which they can be disambiguated, therefore lists are needed for such names. They can be collected easily from a small number of texts in the same domain. 69(110)

Stage ORG PER LOC Sure-fire rule R:42 P:98 R:40 P:99 R:36 P:96 Part. match 1 R:75 P:98 R:80 P:99 R:69 P:93 Relaxed rules (use gaz.) R:83 P:96 R:90 P:98 R:86 P:93 Part. match 2 R:85 P:96 R:93 P:97 R:88 P:93 Title assignment R:91 P:95 R:95 P:97 R:95 P:93 70(110)

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. 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 An instance was put in the training set if it is classified with high confidence by the decision list and also recognised by Identifinder. Training set was 25,000 instances 71(110)

Fine-grained Classification of NEs (2)
Word frequency features – how often the words surrounding the target instance occur with a specific category in training 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 # Position N-gram Category Freq. 1 Previous unigram introduce politician 3 2 entertainer 43 Following bigram into that 4 business 72(110)

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 The topic signature features convey information about the overall context in which each instance exists Due to differing contexts, instances of the same name in a single text were classified differently 73(110)

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 Expts with different feature configurations – 70.4% with all features discussed here Future work: treating finer grained classification as a WSD task (categories are different senses of a person) 74(110)

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 language-independent NE recognition 76(110)

Analysis of the NE Task in Multiple Languages [Palmer&Day 97]
Time/ Date Numeric exprs. Org/Per/ Loc Chinese 4454 17.2% 1.8% 80.9% 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 3579 24.6% 72.5% 77(110)

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 78(110)

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 79(110)

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) 80(110)

Multilingual support - Alembic
Japanese example 81(110)

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) 82(110)

Multilingual Data - GATE
All processing, visualisation and editing tools use GUK 83(110)

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’) 84(110)

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 85(110)

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 86(110)

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 87(110)

ML for NE at CONLL (3) English (f-measure) German (f-measure)
Baseline % (list lookup of entities with 1 class in training data) 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% 88(110)

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 89(110)

Language categorisation
LDC – survey of 300 largest languages (by population) to establish what resources are available Classification dimensions: Dictionaries, news texts, parallel texts, e.g., Bible Script, orthography, words separated by spaces 90(110)

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 91(110)

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 92(110)

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 93(110)

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 94(110)

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 95(110)

96(110)

Evaluation Results Cebuano English Baseline P R F Person 71 65 68 36
Org 75 73 31 47 38 Location 78 76 7 12 Date 83 100 92 42 58 49 Total 79 77.5 45 41.7 43 97(110)

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) 99(110)

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) 100(110)

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) 101(110)

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 102(110)

ACE Example GENERIC="FALSE" entity_type = "ORGANIZATION">
<entity ID="ft-airlines-27-jul " GENERIC="FALSE" entity_type = "ORGANIZATION"> <entity_mention ID="M003" TYPE = "NAME" string = "National Air Traffic Services"> </entity_mention> <entity_mention ID="M004" string = "NATS"> <entity_mention ID="M005" TYPE = "PRO" string = "its"> <entity_mention ID="M006" string = "Nats"> </entity> 103(110)

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. 104(110)

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: ACE also includes other IE tasks, for further details see Doug Appelt’s presentation: 105(110)

Evaluating Richer NE Tagging
Need for new metrics when evaluating hierarchy/ontology-based 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 106(110)

Further Reading Aberdeen J., Day D., Hirschman L., Robinson P. and Vilain M MITRE: Description of the Alembic System Used for MUC-6. MUC-6 proceedings. Pages Columbia, Maryland 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 , 1999 Carreras X., Màrquez L., Padró 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 107(110)

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 , 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) 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. Pages MIT Press. Cambridge, MA. 1996 Mikheev A., Grover C. and Moens M. Description of the LTG System Used for MUC-7. 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 108(110)

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 , 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. 109(110)

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 , 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 CS 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. 110(110)

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