Metadata Extraction: Human Language Technology and the Semantic Web

1. Metadata Extraction: Human Language Technology and the Semantic Web http://gate.ac.uk/http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva Valentin Tablan Diana Maynard SEKT meeting, London, 21 January 2004

2. Gartner, December 2002: taxonomic and hierachical knowledge mapping and indexing will be prevalent in almost all information-rich applications through 2012 more than 95% of human-to-computer information input will involve textual language A contradiction: formal knowledge in semantics-based systems vs. ambiguous informal natural language The challenge: to reconcile these two opposing tendencies The Knowledge Economy and Human Language 2(109)

3. HLT and Knowledge: Closing the Language Loop KEY MNLG: Multilingual Natural Language GenerationOIE: Ontology-aware Information ExtractionAIE: Adaptive IECLIE: Controlled Language IE (M)NLG Semantic Web; Semantic Grid;Semantic Web Services Formal Knowledge(ontologies andinstance bases) HumanLanguage OIE (A)IE ControlledLanguage CLIE 3(109)

4. Structure of the Tutorial • Information Extraction - definition • Evaluation – corpora & metrics • IE approaches – some examples • Rule-based approaches • Learning-based approaches • Semantic Tagging • Using “traditional” IE • Ontology-based IE • Platforms for large-scale processing • Language Generation

5. Information Extraction (IE) pulls facts and structured information from the content of large text collections. Contrast IE and Information Retrieval NLP history: from NLU to IE Progress driven by quantitative measures MUC: Message Understanding Conferences ACE: Automatic Content Extraction Information Extraction 5(109)

6. Held in 1997, around 15 participants inc. 2 UK. Broke IE down into component tasks: NE: Named Entity recognition and typing CO: co-reference resolution TE: Template Elements TR: Template Relations ST: Scenario Templates MUC-7 tasks 6(109)

7. 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. An Example 7(109)

8. 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%) Performance levels 8(109)

9. NE 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 What are Named Entities? 9(109)

10. 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 What are Named Entities (2) 10(109)

11. 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 What are NOT NEs (MUC-7) 11(109)

12. 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" Basic Problems in NE 12(109)

13. 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 More complex problems in NE 13(109)

14. Structure of the Tutorial • Information Extraction - definition • Evaluation – corpora & metrics • IE approaches – some examples • Rule-based approaches • Learning-based approaches • Semantic Tagging • Using “traditional” IE • Ontology-based IE • Platforms for large-scale processing • Language Generation 14(109)

15. 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! Corpora and System Development 15(109)

16. MUC-6 and MUC-7 corpora - English CONLL shared task corpora http://cnts.uia.ac.be/conll2003/ner/ - NEs in English and Germanhttp://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/ Some NE Annotated Corpora 16(109)

17. 100 documents in SGML News domain Named Entities: 1880 Organizations (46%) 1324 Locations (32%) 887 Persons (22%) Inter-annotator agreement very high (~97%) http://www.itl.nist.gov/iaui/894.02/related_projects/muc/proceedings/muc_7_proceedings/marsh_slides.pdf The MUC-7 corpus 17(109)

18. <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. The MUC-7 Corpus (2) 18(109)

19. MUC NE tags 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 Rolls together the NE and CO tasks Domain- and genre-independent approaches ACE corpus contains newswire, broadcast news (ASR output and cleaned), and newspaper reports (OCR output and cleaned) ACE – Towards Semantic Tagging of Entities 19(109)

20. 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 ACE Entities 20(109)

21. <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> ACE Example 21(109)

22. Annotation Tools: Alembic, GATE, ... 22(109)

23. 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 Performance Evaluation 23(109)

24. 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 The Evaluation Metric 24(109)

25. We may also want to take account of partially correct answers: Precision = Correct + ½ Partially correct Correct + Incorrect + Partial Recall = Correct + ½ Partially correctCorrect + Missing + Partial Why: NE boundaries are often misplaced, sosome partially correct results The Evaluation Metric (2) 25(109)

26. The GATE Evaluation Tool 26(109)

27. 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 Corpus-level Regression Testing 27(109)

28. Regression Testing (2) At corpus level – GATE’s corpus benchmark tool – tracking system’s performance over time 28(109)

29. Challenge: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 29(109)

30. Detection of entities and events, given a target ontology of the domain. Disambiguation of the entities and events from the documents with respect to instances in the given ontology. For example, measuring whether the IE correctly disambiguated “Cambridge” in the text to the correct instance: Cambridge, UK vs Cambridge, MA. Decision when a new instance needs to be added to the ontology, because the text contains a new instance, that does not already exist in the ontology. SW IE Evaluation tasks 30(109)

31. Structure of the Tutorial • Information Extraction - definition • Evaluation – corpora & metrics • IE approaches – some examples • Rule-based approaches • Learning-based approaches • Semantic Tagging • Using “traditional” IE • Ontology-based IE • Platforms for large-scale processing • Language Generation 31(109)

32. 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!) Two kinds of IE approaches 32(109)

33. 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 NE Baseline: list lookup approach 33(109)

34. 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 Shallow parsing approach using internal structure 34(109)

35. 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] Problems ... 35(109)

36. 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. Shallow parsing with context 36(109)

37. [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] Examples of context patterns 37(109)

38. Created as part of GATE 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 NE corpus Example Rule-based System - ANNIE 38(109)

39. NE Components The ANNIE system – a reusable and easily extendable set of components 39(109)

40. 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 Gazetteer lists for rule-based NE 40(109)

41. 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. The Named Entity Grammars 41(109)

42. 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” } 42(109)

43. Named Entities in GATE 43(109)

44. 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] Using co-reference to classify ambiguous NEs 44(109)

45. Named Entity Coreference 45(109)

46. Structure of the Tutorial • Information Extraction - definition • Evaluation – corpora & metrics • IE approaches – some examples • Rule-based approaches • Learning-based approaches • Semantic Tagging • Using “traditional” IE • Ontology-based IE • Platforms for large-scale processing • Language Generation 46(109)

47. Approaches: Train ML models on manually annotated text Mixed initiative learning Used for producing training data Used for producing working systems ML Methods Symbolic learning: rules/decision trees induction Statistical models: HMMs, Bayesian methods, Maximum Entropy Machine Learning Approaches 47(109)

48. Instances (tokens, entities) Occurrences of a phenomenon Attributes (features) Characteristics of the instances Classes Sets of similar instances ML Terminology 48(109)

49. The task can be broken into several subtasks (that can use different methods): Boundary detection Entity classification into NE types Different models for different entity types Several models can be used in competition. Some algorithms perform better on little data while others are better when more training is available Methodology 49(109)

50. Boundaries (and entity types) notations S(-XXX), E(-XXX) <S-ORG/>U.N.<E-ORG/> official <S-PER/>Ekeus<E-PER/> heads for <S-LOC/>Baghdad<E-LOC/>. IOB notation (Inside, Outside, Beginning_of) U.N. I-ORG official O Ekeus I-PER heads O for O Baghdad I-LOC .O Translations between the two conventions are straight-forward Methodology (2) 50(109)