Human Language Technology for the Semantic Web http

Human Language Technology for the Semantic Web http: //gate. ac. uk/ http: //nlp. shef. ac. uk/ Hamish Cunningham Kalina Bontcheva Diana Maynard Valentin Tablan ESWS, Crete, May 2004 [This work has been supported by AKT (http: //aktors. org/) and SEKT (http: //sekt. semanticweb. org/)] (130)

Are you wasting your time? 2(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 3(130)

The Knowledge Economy and Human Language Gartner, December 2002: • taxonomic and hierarchical knowledge mapping and indexing will be prevalent in almost all informationrich 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 4(130)

HLT & Knowledge: Closing the Language Loop (M)NLG Human Language KEY MNLG: Multilingual Natural Language Generation OBIE: Ontology-Based Information Extraction AIE: Adaptive & Mixed-Initiative IE CLIE: Controlled Language IE Formal Knowledge (ontologies and instance bases) OBIE (A)IE Controlled Language CLIE 5(130) Semantic Web; Semantic Grid; Semantic Web Services

Background and Examples (1) • Like other areas of computer science, HLT has typical data structures and infrastructure requirements • Annotation: associating arbitrary data with areas of text or speech • Defacto standard: Stand-off Markup (e. g. TEI/XCES, NITE, ATLAS, GATE) • Other issues: visualisation and editing; persistence and search; metrics; component model; baseline NLP tools; . . . • To cut a long story short: HLT has a lot of T underneath it which comes in many shapes and sizes 6(130)

Background and Examples (2) Infrastructure & (many) examples in this tutorial: • GATE, a General Architecture for Text Engineering: architecture, framework & IDE Why? • I happen to know a little about it • Free software, relatively comprehensive, widely used, has extensive Semantic Web support • It means we can ignore the infrastructural issues Not a claim that it is the best or only in all cases! 7(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt]

Information Extraction (1) • 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 (if you can’t score, why not move the goalposts? ) 9(130)

Information Extraction (2) • When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the stage of science. (Kelvin) • Not everything that counts can be counted, and not everything that can be counted counts. (Einstein) • IE progress driven by quantitative measures • MUC: Message Understanding Conferences • ACE: Automatic Content Extraction 10(130)

MUC-7 tasks 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 11(130)

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: "rocket", "Tuesday", "Dr. Head“, "We Build Rockets" • CO: "it" = rocket; "Dr. Head" = "Dr. Big Head" • TE: the rocket is "shiny red" and Head's "brainchild". • TR: Dr. Head works for We Build Rockets Inc. • ST: rocket launch event with various participants 12(130)

Performance levels • Vary according to text type, domain, scenario, language • NE: up to 97% (tested in English, Spanish, Japanese, Chinese, etc. ) • CO: 60 -70% resolution • TE: 80% • TR: 75 -80% • ST: 60% (but: human level may be only 80%) 13(130)

What are Named Entities? • 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 14(130)

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 15(130)

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 16(130)

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" 17(130)

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 Ø Tell me more about Leonardo Ø Da Vinci 18(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 19(130)

Corpora and System Development • “Gold standard” data created by manual annotation • Corpora are divided typically into a training and testing portion • Rules and/or learning algorithms are developed or 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 (typical routine: 10 -fold cross validation) • 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! 20(130)

Some NE Annotated Corpora • MUC-6 and MUC-7 corpora - English • CONLL shared task corpora http: //cnts. uia. ac. be/conll 2003/ner/ NEs in English and German http: //cnts. uia. ac. be/conll 2002/ner/ NEs in Spanish and Dutch • TIDES surprise language exercise (NEs in Cebuano and Hindi) • ACE – English http: //www. ldc. upenn. edu/Projects/ACE/ 21(130)

The MUC-7 corpus • 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_project s/muc/proceedings/muc_7_proceedings/marsh_ slides. pdf 22(130)

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 12 th launched in darkness. 23(130)

ACE – Towards Semantic Tagging of Entities • 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) 24(130)

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 25(130)

ACE Example <entity ID="ft-airlines-27 -jul-2001 -2" GENERIC="FALSE" entity_type = "ORGANIZATION"> <entity_mention ID="M 003" TYPE = "NAME" string = "National Air Traffic Services"> </entity_mention> <entity_mention ID="M 004" TYPE = "NAME" string = "NATS"> </entity_mention> <entity_mention ID="M 005" TYPE = "PRO" string = "its"> </entity_mention> <entity_mention ID="M 006" TYPE = "NAME" string = "Nats"> </entity_mention> </entity> 26(130)

Annotation Tools (1): GATE 27(130)

Annotation Tools (2): Alembic 28(130)

Performance Evaluation • Evaluation metric – mathematically defines how to measure the system’s performance against 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 29(130)

Evaluation Metrics • Most common are “Precision” and “Recall” • Precision = correct answers/answers produced • Recall = correct answers/total possible correct answers • Trade-off between precision and recall • F-Measure = (β 2 + 1)PR / β 2 R + P [van Rijsbergen 75] • β reflects the weighting between precision and recall, typically β=1 • Some tasks sometimes use other metrics, e. g. : – false positives (not sensitive to doc richness) – cost-based (good for application-specific adjustment) 30(130)

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 31(130)

The GATE Evaluation Tool 32(130)

Corpus-level Regression Testing • Need to track system’s performance over time • When a change is made we want to know implications over whole 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(130)

Regression Testing (2) At corpus level – GATE’s corpus benchmark tool – tracking system’s performance over time 34(130)

SW IE Evaluation tasks • 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. 35(130)

Challenge: 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 36(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 37(130)

Two kinds of IE 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!) 38(130)

A) NE 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 39(130)

B) Shallow parsing approach using 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 40(130)

Problems. . . • 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] 41(130)

C) Shallow parsing 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. 42(130)

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] headquarters in [LOCATION] price of [ORGANIZATION] sale of [ORGANIZATION] investors in [ORGANIZATION] is worth [MONEY] [JOBTITLE] [PERSON], [JOBTITLE] 43(130)

Example Rule-based System - ANNIE • ANNIE – A Nearly-New IE system • A version distributed 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 • A reusable and easily extendable set of components 44(130)

NE Components 45(130)

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 {Gmb. H, Ltd, Inc, …} • Produces Lookup results of the given kind 46(130)

The Named Entity Transducers • 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 contextual information • Finds person names, locations, organisations, dates, addresses. 47(130)

NE Rule in 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: Company 1 Priority: 25 ( ( {Token. orthography == upper. Initial} )+ //from tokeniser {Lookup. kind == company. Designator} //from gazetteer lists ): match --> : match. Named. Entity = { kind=company, rule=“Company 1” } 48(130)

49(130) Named Entities in GATE

Using co-reference to classify ambiguous NEs • Orthographic co-reference module 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] 50(130)

Named Entity Coreference 51(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 52(130)

Machine Learning Approaches • 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 53(130)

ML Terminology • Instances (tokens, entities) Occurrences of a phenomenon • Attributes (features) Characteristics of the instances • Classes Sets of similar instances 54(130)

Methodology • 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 55(130)

Methodology (2) 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. official Ekeus heads for Baghdad . I-ORG O I-PER O O I-LOC O – Translations between the two conventions are straight-forward 56(130)

Features • Document structure • Linguistic features – – – Original markup – Paragraph/sentence structure • Surface features – Token length – Capitalisation – Token type (word, punctuation, symbol) POS Morphology Syntax Lexicon data • Semantic features – Ontological class • ETC –… • Feature selection – the most difficult part • Some automatic scoring methods can be used 57(130)

Mixed Initiative Learning • • Human – computer interaction Speeds up the creation of training data Can be used for corpus/system creation Example implementations: – Alembic [Day et al’ 97] and later – Amilcare [Ciravegna’ 03] 58(130)

Mixed Initiative Learning (2) User annotates System learns P>t 1 System annotates User corrects P>t 2 System annotates 59(130) System learns

Example 1: Alembic, Day et al 1997 • Mixed initiative approach implemented in Alembic Workbench • Bootstrapping procedure – use already tagged data to pre-annotate new documents • Transforms the process from tagging to review • Finally, the trained system can be used on its own 60(130)

Mixed-Initiative text annotation User can also edit the induced rules and write new ones Brill-style learning – generate-andtest 61(130)

Considerations • Too high recall and the human might become over-reliant on the system annotations • Too high precision might have similar effect • “Theory-creep” – the choices of the human annotator are increasingly influenced by the machine’s and might deviate from the task definition – measure inter-annotator agreement 62(130)

Example 2: Amilcare & Melita • Amilcare: rule-learning algorithm – Tagging rules – learn to insert tags in the text, given training examples – Correction rules – learn to move already inserted tags to their correct place in the text • • Learns begin and end tags independently Melita support adaptive IE Applied in Sem. Web context (see below) Being extended as part of the EU-funded DOT. KOM project towards KM and Sem. Web applications 63(130)

Comparison of Alembic & Melita • The life cycle of user tagging, the machine learning, then making suggestions which user corrects, is very similar in Melita and Alembic • Alembic is more oriented towards NLP developers, while Melita – more towards endusers • Melita considers timeliness and intrusiveness as criteria, while Alembic does not (possibly due to performance bottleneck from old hardware) • Both acknowledge but do not address problems with over-reliance on machine annotations • From ML perspective the two are very similar – rule-learning 64(130)

Eg. 3: GATE Machine Learning support • Uses classification. [Attr 1, Attr 2, Attr 3, … Attrn] Class • Classifies annotations. (Documents can be classified as well using a 1 -to 1 relation with annotations. ) • Annotations of a particular type are selected as instances. • Attributes refer to features of the instance annotations or their context. • Generic implementation for attribute collection – can be linked to any ML engine. • ML engines currently integrated: WEKA and Ontotext’s HMM. 65(130)

Implementation Machine Learning PR in GATE. Has two functioning modes: – training – application Uses an XML file for configuration: <? xml version="1. 0" encoding="windows-1252"? > <ML-CONFIG> <DATASET> … </DATASET> <ENGINE>…</ENGINE> <ML-CONFIG> 66(130)

Attributes Collection Instances type: Token 67(130)

Dataflow Feature Collection Plain text documents Tokeniser Gazetteer POS Tagger Lexicon Lookup Semantic Tagger etc… Results Converter GATE ML Library NLP Pipeline Engine Interface Machine Learning Engine 68(130) Annotated documents

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 69(130)

Towards Semantic Tagging of Entities • The MUC NE task tags selected segments of text whenever that text represents the name of an entity. • Semantic tagging - view as mentions of the underlying instances from the ontology • Identify which mentions in the text refer to which instances in the ontology, e. g. , – Tony Blair, Mr. Blair, he, the prime minister, he – Gordon Brown, he, Mr. Brown, the chancellor 70(130)

Tasks • Identify entity mentions in the text • Reference disambiguation – Add new instances if needed – Disambiguate wrt instances in the ontology • Identify instances of attributes and relations – take into account what are allowed given the ontology, using domain&range as constraints 71(130)

Example XYZ was established on 03 November 1978 in London. It opened a plant in Bulgaria in … Ontology & KB Company Location HQ City type XYZ part. Of Country type HQ London establ. On type part. Of “ 03/11/1978” UK 72(130) Bulgaria

Classes, instances & metadata “Gordon Brown met George Bush during his two day visit. after Classes+instances before … Entity Person Job-title Bush president minister chancellor … <metadata> <DOC-ID>http: //… 1. html</DOC-ID> <Annotation> <s_offset> 0 </s_offset> <e_offset> 12 </e_offset> G. Brown <string>Gordon Brown</string> <class>…#Person</class> <inst>…#Person 12345</inst> </Annotation> <s_offset> 18 </s_offset> <e_offset> 32 </e_offset> <string>George Bush</string> <class>…#Person</class> <inst>…#Person 67890</inst> </Annotation> 73(130) </metadata>

Classes, instances & metadata (2) “Gordon Brown met Tony Blair to discuss the university tuition fees. after Classes+instances before … <metadata> <DOC-ID>http: //… 2. html</DOC-ID> <Annotation> <s_offset> 0 </s_offset> Entity <e_offset> 12 </e_offset> T. Blair Person <string>Gordon Brown</string> <class>…#Person</class> G. Brown <inst>…#Person 12345</inst> Job-title G. Bush </Annotation> <Annotation> president <s_offset> 18 </s_offset> minister <e_offset> 30 </e_offset> chancellor <string>Tony Blair</string> <class>…#Person</class> <inst>…#Person 26389</inst> … </Annotation> 74(130) </metadata>

Why not put metadata in ontologies? • Can be encoded in RDF/OWL, etc. but does it need to be put as instances in the ontology? • Typically we do not need to reason with it – Reasoning happens in the ontology when the new instances of classes and properties are added, but the metadata statements are different from them, they only refer to them • A lot more metadata than instances – Millions of metadata statements, thousands of instances, hundreds of concepts • Different access required: – By offset (give me all metadata of the first paragraph) – Efficient metadata-wide statistics based on strings – not an operation that people would do on other concepts – Mixing with keyword-based search using IR-style indexing 75(130)

Metadata Creation with IE • Semantic tagging creates metadata • Stand-off or part of document • Semi-automatic – One view (given by the user, one ontology) – More reliable • Automatic metadata creation – Many views – change with ontology, re-train IE engine for each ontology – Always up to date, if ontology changes – Less reliable 76(130)

Problems with “traditional” IE for metadata creation • S-CREAM – Semi-automatic CREAtion of Metadata [Handschuh et al’ 02] • Semantic tags from IE need to be mapped to instances of concepts, attributes or relations • Most ML-based IE systems do not deal well with relations, mainly entities • Amilcare does not handle anaphora resolution, GATE has such component but not used here • Implemented a discourse model with logical rules – LASIE used discourse model with domain ontology – problem is robustness and domain portability 77(130)

Example [Handschuh et al’ 02] S-CREAM, EKAW’ 02 78(130)

S-CREAM: Discourse Rules • Rules to attach instances only when the ontology allows that (e. g. , prices) • Attach tag values to the nearest preceding compatible entity (e. g. , prices and rooms) • Create a complex object between two concept instances if they are adjacent (e. g. , rate – number followed by currency) • Experienced users can write new rules 79(130)

Challenges for IE for Sem. Web • • • Portability – different and changing ontologies Different text types – structured, free, etc. Utilise ontology information where available Train from small amount of annotated text Output results wrt the given ontology – bridge the gap demonstrated in S-CREAM • Learn/Model at the right level – ontologies are hierarchical and data will get sparser the lower we go [DOT. KOM http: //nlp. shef. ac. uk/dot. kom/] 80(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE (OBIE) – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 81(130)

Eg. 1: GATE & metadata extraction • Combines learning and rule-based methods • Allows combination of IE and IR • Enables use of large-scale linguistic resources for IE, such as Word. Net • Supports ontologies as part of IE applications - Ontology-Based IE (OBIE) 82(130)

Ontology Management in GATE 83(130)

Information Retrieval Currently based on the Lucene IR engine – useful for combining semantic and keyword-based search 84(130)

85(130) Word. Net support

Populating Ontologies with IE 86(130)

Example 2: OBIE in h-Tech. Sight • h. Tech. Sight project – using Ontology-Based IE for semantic tagging of job adverts, news and reports in chemical engineering domain • Aim is to track technological change over time through terminological analysis • Fundamental to the application is a domain-specific ontology • Terminological gazetteer lists are linked to classes in the ontology • Rules classify the mentions in the text wrt the domain ontology • Annotations output into a database or as an ontology 87(130)

88(130)

89(130)

Exported Database 90(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 91(130)

Platforms for Large-Scale Metadata Creation • Allow use of corpus-wide statistics to improve metadata quality, e. g. , disambiguation • Automated alias discovery • Generate Sem. Web output (RDF, OWL) • Stand-off storage and indexing of metadata • Use large instance bases to disambiguate to • Ontology servers for reasoning and access • Architecture elements: – Crawler, onto storage, doc indexing, query, annotators – Apps: sem browsers, authoring tools, etc. 92(130)

Example 1: Sem. Tag • Lookup of all instances from the ontology (TAP) – 65 K instances • Disambiguate the occurrences as: – One of those in the taxonomy – Not present in the taxonomy • Not very high ambiguity of instances with the same label in TAP – concentrate on the second problem • Use bag-of-words approach for disambiguation • 3 people evaluated 200 labels in context – agreed on only 68. 5% - metonymy • Placing labels in the taxonomy is hard Dill et al, Sem. Tag and Seeker. WWW’ 03 93(130)

Seeker • High-performance distributed infrastructure • 128 dual-processor machines with separate ½ terabyte of storage • Each node runs approx. 200 documents per sec. • Service-oriented architecture – Vinci (SOAP) Dill et al, Sem. Tag and Seeker. WWW’ 03 94(130)

Example 2: OBIE in KIM • The ontology (KIMO) and 86 K/200 K instances KB • High ambiguity of instances with the same label – need for disambiguation step • Lookup phase marks mentions from the ontology • Combined with rule-based IE system to recognise new instances of concepts and relations • Special KB enrichment stage where some of these new instances are added to the KB • Disambiguation uses an Entity Ranking algorithm, i. e. , priority ordering of entities with the same label based on corpus statistics (e. g. , Paris) Popov et al. KIM. ISWC’ 03 95(130)

OBIE in KIM (2) Popov et al. KIM. ISWC’ 03 96(130)

Comparison between Sem. Tag & KIM • Sem. Tag only aims for accuracy (precision) of classification of the annotated entities • KIM also aims for coverage (recall) – whether all possible mentions of entities were found • Trade-off – sometimes finding some is enough • Sem. Tag does not attempt to discover and expand the KB with new instances (e. g. , new company) – the reason why KIM uses IE, not simple KB lookup • i. e. OBIE is often needed for ontology population, not just metadata creation 97(130)

Two Annotation Scenarios (1) • Getting the instances and the relations between them is enough, maybe not all mentions in the text are covered, but compensated by giving access to this info from the annotated text 98(130)

Example “Gordon Brown met president Bush during his two day visit. Afterwards George Bush said… … The system Entity G. Brown Person Job-title … Benchmark Entity Person Bush Job-title president minister chancellor … Score: 100% G. Brown Bush 99(130) …

Two Annotation Scenarios (2) • Exhaustive annotation is required, so all occurrences of all instances and relations are needed • Allows sentence and paragraph-level exploration, rather than document-level as in the previous scenario • Harder to achieve • Distinction between these scenarios needs to be made in the metadata annotation tools/KM tools using IE 100(130)

Example “Gordon Brown met president Bush during his two day visit. Afterwards George Bush said… <metadata> <Annotation> <s_offset> 0 </s_offset> <e_offset> 12 </e_offset> <class>…#Person</class> <inst>…#Person 12345</inst> </Annotation> <s_offset> 61 </s_offset> <e_offset> 72 </e_offset> <class>…#Person</class> <inst>…#Person 1267</inst> </Annotation> </metadata> Score: 66% <Annotation> <s_offset> 0 </s_offset> <e_offset> 12 </e_offset> <class>…#Person</class> <inst>…#Person 12345</inst> </Annotation> <s_offset> 18 </s_offset> <e_offset> 32 </e_offset> <class>…#Person</class> <inst>…#Person 1267</inst> </Annotation> <s_offset> 61 </s_offset> <e_offset> 72 </e_offset> <class>…#Person</class> <inst>…#Person 1267</inst> </Annotation> 101(130) </metadata>

Eg. 3: SWAN: a Semantic Web Annotator • Collaboration between DERI/NUIG, Onto. Text and USFD, hosted at DERI • GATE + KIM + SECO • • Custom indexing of news or other web fractions Quantitative media reporting Annotated web workbench service Custom knowledge services • Demo and poster at ESWS 102(130)

SWAN Logical Architecture Web IE (64 bit) Focussed crawling Focussed crawling Focussed crawling IE crawling (32 bit) Annotation (Oracle) Web UI, Web services Knowledge base (Sesame) Service Users 103(130) UI Users

Cluster Controller 104(130)

Semantic Reference Disambiguation • Possible approaches: – Vector-space models – compare context similarity – runs over a corpus • Sem. Tag • Bagga’s cross-document coreference work – Communities of practise approach from KM – Identity criteria from the ontology based on properties, e. g. , date_of_birth, name 105(130)

Why disambiguation is hard – not all knowledge is explicit in text Paris fashion week underway as cancellations continue By Jo Johnson and Holly Finn - Oct 07 2001 18: 48: 17 (FT) Even as Paris fashion week opened at the weekend, the cancellations and reschedulings were still trickling in over the fax machines: Loewe, the leather specialists owned by LVMH empire, is not showing, Cerruti, the Italian tailor, is downscaling to private viewings, Helmut Lang, master of the sharp suit, is cancelling his catwalk. The Oscar de la Renta show, for example, which had been planned for September 11 th in New York, and which might easily enough have moved over to Paris instead, is not on the schedule. When the Dominican Republic-born designer consulted America Vogue's influential editor, Anna Wintour, she reportedly told him it would be unpatriotic to decamp. 106(130)

Structure of the Tutorial 1. 2. 3. 4. Motivation, background Information Extraction - definition Evaluation – corpora & metrics IE approaches – some examples – Rule-based approaches – Learning-based approaches 5. Semantic Tagging – Using “traditional” IE – Ontology-based IE – Platforms for large-scale processing 6. Language Generation [Slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt] 107(130)

Natural Language Generation • NLG is: – “subfield of AI and CL that is concerned with the construction of computer systems that can produce understandable texts in English or other human languages from some underlying linguistic representation of information” [Reiter&Dale’ 97] – NLG techniques are applied also for producing speech, e. g. , in speech dialogue systems 108(130)

Natural Language Generation Ontology/KB/Database Text 109(130) Lexicons + Grammars

Requirements Analysis • Create a corpus of target texts and (if possible) their input representations • Analyse the information content – Unchanging texts: thank you, hello, etc. – Directly available data: timetable of buses – Computable data: number of buses – Unavailable data: not in the system’s KB/DB 110(130)

NLG Tasks 1. 2. 3. 4. 5. 6. Content determination Discourse planning Sentence aggregation Lexicalisation Referring expression generation Linguistic realisation 111(130)

Content determination • What information to include in the text – filtering and summarising input data into a formal knowledge representation • Application dependent • Example [ project: start_date: end_date: participants: AKT October-2000 October-2006 {A, E, OU, So, Sh}] 112(130)

Discourse Planning • Determine ordering and structure over the knowledge to be generated • Theories of discourse – how texts are structured • Influences text readability • Result: tree structure imposing ordering over the predicates and possibly providing discourse relations 113(130)

Example Root SEQUENCE Project participants LIST Participant descr. ELABORATION [project: AKT duration: 6 yrs] [project: AKT participant: Shef] Participant descr. ELABORATION [univ: Shef [project: AKT Web-page: URL] participant: OU] 114(130) … …

Planning-Based Approaches • Use AI-style planners (e. g. , [Moore & Paris 93] – Discourse relations (e. g. , ELABORATION) are encoded as planning operators – Preconditions specify when the relation can apply – Planning starts from a top-level goal, e. g. , defineproject(X) • Computationally expensive and require a lot of knowledge – problem for real-world systems 115(130)

Schema-Based Approaches • Capture typical text structuring patterns in templates (derived from corpus), e. g. , [Mc. Keown 85] • Typically implemented as RTN • Variety comes from different available knowledge for each entity • Reusable ones available: Exemplars • Example: Describe-Project-Schema -> Sequence([duration], Proj. Participants-Schema) 116(130)

Sentence Aggregation • Determine which predicates should be grouped together in sentences • Less understood process • Default: each predicate can be expressed as a sentence, so optional step • SPOT: trainable planner • Example: AKT is a 6 -year project with 5 participants: • Sheffield (URL) • OU … 117(130)

Lexicalisation • Choosing words and phrases to express the concepts and relations in predicates • Trivial solution: 1 -1 mapping between concepts/relations and lexical entries • Variation is useful to avoid repetitiveness and also convey pragmatic distinctions (e. g. formality) 118(130)

Referring Expression Generation • Choose pronouns/phrases to refer to the entities in the text • Example: he vs Mr Smith vs John Smith, the president of XXX Corp. • Depends on what is previously said – He is only appropriate if the person is already introduced in the text 119(130)

Linguistic Realisation • Use grammar to generate text which is grammatical, i. e. , syntactically and morphologically correct • Domain-independent • Reusable components are available – e. g. , Real. Pro, FUF/SURGE • Example: – Morphology: participant -> participants – Syntactic agreement: AKT starts on … 120(130)

Example: a GATE-based generator • Input – The MIAKT ontology – The RDF file for the given case – The MIAKT lexicon • Output – GATE document with the generated text 121(130)

Lexicalising Concepts and Instances 122(130)

Example RDF Input <rdf: Description rdf: about='c: breast_cancer_ontology. daml#01401_patient'> <rdf: type rdf: resource='c: breast_cancer_ontology. daml#Patient'/> <NS 2: has_age>68</NS 2: has_age> <NS 2: involved_in_ta rdf: resource='c: breast_cancer_ontology. daml#tasoton-1069861276136'/> </rdf: Description> <rdf: Description rdf: about='c: breast_cancer_ontology. daml#01401_mammography'> <rdf: type rdf: resource='c: breast_cancer_ontology. daml#Mammography '/> <NS 2: carried_out_on rdf: resource='c: breast_cancer_ontology. daml#01401_patient'/> <NS 2: has_date>22 9 1995</NS 2: has_date> <NS 2: produce_result rdf: resource='c: breast_cancer_ontology. daml#image_01401_right_cc'/> </rdf: Description> <rdf: Description rdf: about='c: breast_cancer_ontology. daml#image_01401_right_cc'> <NS 2: image_file>cancer/case 0140/C_0140_1. RIGHT_CC. LJPEG</NS 2: image_file> <rdf: type rdf: resource='c: breast_cancer_ontology. daml#Right_CC_Image '/> <NS 2: has_lateral rdf: resource='c: breast_cancer_ontology. daml#lateral_right '/> <NS 2: view_of_image rdf: resource='c: breast_cancer_ontology. daml#craniocaudal_view '/> <NS 2: contains_entity rdf: resource='c: breast_cancer_ontology. daml#01401_right_cc_abnor_1'/> </rdf: Description> <rdf: Description rdf: about='c: breast_cancer_ontology. daml#01401_right_cc_abnor_1'> <rdf: type rdf: resource='c: breast_cancer_ontology. daml#Abnormality '/> <NS 2: is_finding rdf: resource='c: breast_cancer_ontology. daml#mass_01401_right_cc_abnor_1'/> <NS 2: has_morph_feature rdf: resource='c: breast_cancer_ontology. daml#shape_mammo_round '/> <NS 2: has_morph_feature rdf: resource='c: breast_cancer_ontology. daml#margin_mammo_microlobulated '/> <NS 2: has_overall_impression rdf: resource='c: breast_cancer_ontology. daml#assessment_probably_malignant '/> </rdf: Description> 123(130)

CASE 0140. RDF The 68 years old patient is involved in a triple assessment procedure. The triple assessment procedure contains a mammography exam. The mammography exam is carried out on the patient on 22 9 1995. The mammography exam produced a right CC image. The right CC image contains an abnormality and it has a right lateral side and a craniocaudal view. The abnormality has a mass, a microlobulated margin , a round shape, and a probably malignant assessment. 124(130)

Further Reading on IE for Sem. Web • • Requirements for Information Extraction for Knowledge Management. http: //nlp. shef. ac. uk/dot. kom/publications. html Information Extraction as a Semantic Web Technology: Requirements and Promises. Adaptive Text Extraction and Mining workshop, 2003. A. Kiryakov, B. Popov, et al. Semantic Annotation, Indexing, and Retrieval. 2 nd International Semantic Web Conference (ISWC 2003), http: //www. ontotext. com/publications/index. html#Kiryakov. Et. Al 2003 S. Handschuh, S. Staab, R. Volz: http: //www. aifb. unikarlsruhe. de/WBS/sha/papers/p 273_handschuh. pdf. On Deep Annotation. WWW’ 03. S. Dill, N. Eiron, et al: http: //www. tomkinshome. com/papers/2 Web/semtag. pdf. Sem. Tag and Seeker: Bootstrapping the semantic web via automated semantic annotation. WWW’ 03. E. Motta, M. Vargas-Vera, et al: Mn. M: Ontology Driven Semi-Automatic and Automatic Support for Semantic Markup. : Knowledge Engineering and Knowledge Management (Ontologies and the Semantic Web), (EKAW 02), http: //www. aktors. org/publications/selected-papers/06. pdf K. Bontcheva, A. Kiryakov, H. Cunningham, B. Popov. M. Dimitrov. Semantic Web Enabled, Open Source Language Technology and the Semantic Web, Workshop on NLP and XML (NLPXML-2003). http: //www. gate. ac. uk/sale/eacl 03 -semweb/bontcheva-etal-final. pdf Handschuh, Staab, Ciravegna. S-CREAM - Semi-automatic CREAtion of Metadata (2002) http: //citeseer. nj. nec. com/529793. html 125(130)

Further Reading on “traditional” IE • • • [Day et al’ 97] D. Day, J. Aberdeen, L. Hirschman, R. Kozierok, P. Robinson, and M. Vilain. Mixed-Initiative Development of Language Processing Systems. In Proceedings of the Fifth Conference on Applied Natural Language Processing (ANLP’ 97). 1997. [Ciravegna’ 02] F. Ciravegna, A. Dingli, D. Petrelli, Y. Wilks: User-System Cooperation in Document Annotation based on Information Extraction. Knowledge Engineering and Knowledge Management (Ontologies and the Semantic Web), (EKAW 02), 2002. N. Kushmerick, B. Thomas. Adaptive information extraction: Core technologies for information agents (2002). http: //citeseer. nj. nec. com/kushmerick 02 adaptive. html H. Cunningham, D. Maynard, K. Bontcheva, V. Tablan. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. 40 th Anniversary Meeting of the Association for Computational Linguistics (ACL'02). 2002. D. Maynard, K. Bontcheva and H. Cunningham. Towards a semantic extraction of named entities. Recent Advances in Natural Language Processing, Bulgaria, 2003. Califf and Mooney: Relational Learning of Pattern Matching Rules for Information Extraction http: //citeseer. nj. nec. com/6804. html Borthwick. A. A Maximum Entropy Approach to Named Entity Recognition. Ph. D Dissertation. 1999 Bikel D. , Schwarta R. , Weischedel. R. An algorithm that learns what’s in a name. Machine Learning 34, pp. 211 -231, 1999 Riloff, E. (1996) "Automatically Generating Extraction Patterns from Untagged Text" Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96) , 1996, pp. 1044 -1049. http: //www. cs. utah. edu/%7 Eriloff/psfiles/aaai 96. pdf Daelemans W. and Hoste V. Evaluation of Machine Learning Methods for Natural Language Processing Tasks. In LREC 2002 Third International Conference on Language Resources and Evaluation, pages 755– 760 126(130)

Further Reading on “traditional” IE • • Black W. J. , Rinaldi F. , Mowatt D. Facile: Description of the NE System Used For MUC-7. Proceedings of 7 th Message Understanding Conference, Fairfax, VA, 19 April - 1 May, 1998. 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 40 th 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. Krupka G. R. , Hausman K. Iso. Quest Inc. : Description of the Net. Owl. TM Extractor System as Used for MUC-7. Proceedings of 7 th Message Understanding Conference, Fairfax, VA, 19 April - 1 May, 1998. Mc. Donald 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 21 -39. MIT Press. Cambridge, MA. 1996 Mikheev A. , Grover C. and Moens M. Description of the LTG System Used for MUC-7. Proceedings of 7 th 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 7 th Message Understanding Conference, Fairfax, VA, 19 April - 1 May, 1998 127(130)

Further Reading on multilingual IE • • 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 19 th International Conference on Computational Linguistics (COLING 2002), pp. 967 -973, 2002. Takeuchi K. , Collier N. Use of Support Vector Machines in Extended Named Entity Recognition. The 6 th 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. 128(130)

Further Reading on multilingual IE • • • 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 40 th 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 10 th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2002), 2002. 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. 129(130)

THANK YOU! (for not snoring) The slides: http: //gate. ac. uk/sale/talks/esws 2004 -tutorial. ppt [This work has been supported by AKT (http: //aktors. org/) and SEKT (http: //sekt. semanticweb. org/)] 130(130)