ELABORAZIONE DEL LINGUAGGIO NATURALE SEMANTICA: NAMED ENTITIES RELAZIONI

SEMANTICA MODERNA Sue sottocompiti base – Classificazione di entita’: NAMED ENTITY RECOGNITION (and classification) – Riconoscimento di predicati e loro argomenti: RELATION EXTRACTION

Named Entity Recognition (NER) Input: Apple Inc., formerly Apple Computer, Inc., is an American multinational corporation headquartered in Cupertino, California that designs, develops, and sells consumer electronics, computer software and personal computers. It was established on April 1, 1976, by Steve Jobs, Steve Wozniak and Ronald Wayne. Output: Apple Inc., formerly Apple Computer, Inc., is an American multinational corporation headquartered in Cupertino, California that designs, develops, and sells consumer electronics, computer software and personal computers. It was established on April 1, 1976, by Steve Jobs, Steve Wozniak and Ronald Wayne.

Named Entity Recognition (NER) Locate and classify atomic elements in text into predefined categories (persons, organizations, locations, temporal expressions, quantities, percentages, monetary values, …) Input: a block of text – Jim bought 300 shares of Acme Corp. in Output: annotated block of text – Jim bought 300 shares of Acme Corp. in 2006 – ENAMEX tags (MUC in the 1990s)

THE STANDARD NEWS DOMAIN Most work on NER focuses on – NEWS – Variants of repertoire of entity types first studied in MUC and then in ACE: PERSON ORGANIZATION – GPE LOCATION TEMPORAL ENTITY NUMBER

HOW Two tasks: – Identifying the part of text that mentions a text (RECOGNITION) – Classifying it (CLASSIFICATION) The two tasks are reduced to a standard classification task by having the system classify WORDS

Basic Problems in NER 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”

Problems in NER Category definitions are intuitively quite clear, but there are many grey areas. Many of these grey area are caused by metonymy. Organisation vs. Location : “England won the World Cup” vs. “The World Cup took place in England”. Company vs. Artefact: “shares in MTV” vs. “watching MTV” Location vs. Organisation: “she met him at Heathrow” vs. “the Heathrow authorities”

Approaches to NER: List Lookup System that recognises only entities stored in its lists (GAZETTEERS). Advantages - Simple, fast, language independent, easy to retarget Disadvantages – collection and maintenance of lists, cannot deal with name variants, cannot resolve ambiguity

Approaches to NER: Shallow Parsing Names often have internal structure. These components can be either stored or guessed. location: CapWord + {City, Forest, Center} e.g. Sherwood Forest Cap Word + {Street, Boulevard, Avenue, Crescent, Road} e.g. Portobello Street

Shallow Parsing Approach (E.g., Mikheev et al 1998) External evidence - names are often used in very predictive local contexts Location: “to the” COMPASS “of” CapWord e.g. to the south of Loitokitok “based in” CapWord e.g. based in Loitokitok CapWord “is a” (ADJ)? GeoWord e.g. Loitokitok is a friendly city

Machine learning approaches to NER NER as classification: the IOB representation Supervised methods – Support Vector Machines – Logistic regression (aka Maximum Entropy) – Sequence pattern learning – Hidden Markov Models – Conditional Random Fields Distant learning Semi-supervised methods

THE ML APPROACH TO NE: THE IOB REPRESENTATION

THE ML APPROACH TO NE: FEATURES

FEATURES

Supervised ML for NER Methods already seen – Decision trees – Support Vector Machines Sequence pattern learning (also supervised) – Hidden Markov Models – Maximum Entropy Models – Conditional Random Fields

EVALUATION

TYPICAL PERFORMANCE

NER Evaluation Campaigns English NER-- CoNLL PER/ORG/LOC/MISC – Training set: tokens – Development set: tokens – Test set: tokens Italian NER-- Evalita PER/ORG/LOC/GPE – Development set: tokens – Test set: tokens Mention Detection-- ACE 2005 – 599 documents

CoNLL2003 shared task (1) English and German language 4 types of NEs: – LOC Location – MISC Names of miscellaneous entities – ORG Organization – PER Person Training Set for developing the system Test Data for the final evaluation

CoNLL2003 shared task (2) Data – columns separated by a single space – A word for each line – An empty line after each sentence – Tags in IOB format An example MilanNNPB-NPI-ORG 'sPOSB-NPO playerNNI-NPO GeorgeNNPI-NPI-PER WeahNNPI-NPI-PER meetVBPB-VPO

CoNLL2003 shared task (3) Englishprecision recall F [FIJZ03]88.99%88.54%88.76% [CN03]88.12%88.51%88.31% [KSNM03]85.93%86.21%86.07% [ZJ03]86.13%84.88%85.50% [Ham03]69.09%53.26%60.15% baseline71.91%50.90%59.61%

CURRENT RESEARCH ON NER New domains New approaches: – Semi-supervised – Distant Handling many NE types Integration with Machine Translation Handling difficult linguistic phenomena such as metonymymetonymy

NEW DOMAINS BIOMEDICAL CHEMISTRY HUMANITIES: MORE FINE GRAINED TYPES

Bioinformatics Named Entities Protein DNA RNA Cell line Cell type Drug Chemical

NER IN THE HUMANITIES LOC SITE CULTURE

SEMANTIC INTERPRETATION 2: FROM SENTENCES TO PROPOSITIONS Powell met Zhu Rongji Proposition: meet(Powell, Zhu Rongji ) Powell met with Zhu Rongji Powell and Zhu Rongji met Powell and Zhu Rongji had a meeting... When Powell met Zhu Rongji on Thursday they discussed the return of the spy plane. meet(Powell, Zhu) discuss([Powell, Zhu], return(X, plane)) debate consult join wrestle battle meet(Somebody1, Somebody2)

OTHER ASPECTS OF SEMANTIC INTERPRETATION Identification of RELATIONS between entities mentioned – Focus of interest in modern CL since 1993 or so Identification of TEMPORAL RELATIONS – From about 2003 on QUALIFICATION of such relations (modality, epistemicity) – From about 2010 on

TYPES OF RELATIONS Predicate-argument structure (verbs and nouns) – John kicked the ball Nominal relations – The red ball Relations between events / temporal relations – John kicked the ball and scored a goal

PREDICATE-ARGUMENT STRUCTURE Linguistic Theories – Case Frames – Fillmore  FrameNet – Lexical Conceptual Structure – Jackendoff  LCS – Proto-Roles – Dowty  PropBank – English verb classes (diathesis alternations) - Levin  VerbNet – Talmy, Levin and Rappaport

Fillmore’s Case Theory Sentences have a DEEP STRUCTURE with CASE RELATIONS A sentence is a verb + one or more NPs – Each NP has a deep-structure case A(gentive) I(nstrumental) D(ative) F(actitive) L(ocative) O(bjective) – Subject is no more important than Object Subject/Object are surface structure

THEMATIC ROLES Following on Fillmore’s original work, many theories of predicate argument structure / thematic roles were proposed, among which the best known perhaps – Jackendoff’s LEXICAL CONCEPTUAL SEMANTICS – Dowty’s PROTO-ROLES theory

Dowty’s PROTO-ROLES Event-dependent Prototypes based on shared entailments Grammatical relations such as subject related to observed (empirical) classification of participants Typology of grammatical relations Proto-Agent Proto-Patient

Proto-Agent Properties – Volitional involvement in event or state – Sentience (and/or perception) – Causing an event or change of state in another participant – Movement (relative to position of another participant) – (exists independently of event named) *may be discourse pragmatic

Proto-Patient Properties: – Undergoes change of state – Incremental theme – Causally affected by another participant – Stationary relative to movement of another participant – (does not exist independently of the event, or at all) *may be discourse pragmatic

Semantic role labels: Jan broke the LCD projector. break (agent(Jan), patient(LCD-projector)) cause(agent(Jan), change-of-state(LCD-projector)) (broken(LCD-projector)) agent(A) -> intentional(A), sentient(A), causer(A), affector(A) patient(P) -> affected(P), change(P),… Filmore, 68 Jackendoff, 72 Dowty, 91

VERBNET AND PROPBANK Dowty’s theory of proto-roles was the basis for the development of PROPBANK, the first corpus annotated with information about predicate-argument structure

PROPBANK REPRESENTATION a GM-Jaguar pact that would give *T*-1 the US car maker an eventual 30% stake in the British company Arg0 Arg2 Arg1 give(GM-J pact, US car maker, 30% stake) a GM-Jaguar pact that would give the U.S. car maker an eventual 30% stake in the British company.

ARGUMENTS IN PROPBANK Arg0 = agent Arg1 = direct object / theme / patient Arg2 = indirect object / benefactive / instrument / attribute / end state Arg3 = start point / benefactive / instrument / attribute Arg4 = end point Per word vs frame level – more general?

FROM PREDICATES TO FRAMES In one of its senses, the verb observe evokes a frame called Compliance: this frame concerns people’s responses to norms, rules or practices. The following sentences illustrate the use of the verb in the intended sense: – Our family observes the Jewish dietary laws. – You have to observe the rules or you’ll be penalized. – How do you observe Easter? – Please observe the illuminated signs.

FrameNet FrameNet records information about English words in the general vocabulary in terms of 1.the frames (e.g. Compliance) that they evoke, 2.the frame elements (semantic roles) that make up the components of the frames (in Compliance, Norm is one such frame element), and 3.each word’s valence possibilities, the ways in which information about the frames is provided in the linguistic structures connected to them (with observe, Norm is typically the direct object). theta

NOMINAL RELATIONS

CLASSIFICATION SCHEMES FOR NOMINAL RELATIONS

ONE EXAMPLE (Barker et al1998, Nastase & Spakowicz 2003)

THE TWO-LEVEL TAXONOMY OF RELATIONS, 2

THE SEMEVAL-2007 CLASSIFICATION OF RELATIONS Cause-Effect: laugh wrinkles Instrument-Agency: laser printer Product-Producer: honey bee Origin-Entity: message from outer-space Theme-Tool: news conference Part-Whole: car door Content-Container: the air in the jar

THE MUC AND ACE TASKS Modern research in relation extraction, as well, was kicked-off by the Message Understanding Conference (MUC) campaigns and continued through the Automatic Content Extraction (ACE) and Machine Reading follow- ups MUC: NE, coreference, TEMPLATE FILLING ACE: NE, coreference, relations

TEMPLATE-FILLING

EXAMPLE MUC: JOB POSTING

THE ASSOCIATED TEMPLATE

AUTOMATIC CONTENT EXTRACTION (ACE)

ACE: THE DATA

ACE: THE TASKS

RELATION DETECTION AND RECOGNITION

ACE: RELATION TYPES

OTHER PRACTICAL VERSIONS OF RELATION EXTRACTION Biomedical domain (BIONLP, BioCreative) Chemistry Cultural Heritage

THE TASK OF SEMANTIC RELATION EXTRACTION

SEMANTIC RELATION EXTRACTION: THE CHALLENGES

HISTORY OF RELATION EXTRACTION Before 1993: Symbolic methods (using knowledge bases) Since then: statistical / heuristic based methods – From 1995 to around 2005: mostly SUPERVISED – More recently: also quite a lot of UNSUPERVISED / SEMI SUPERVISED techniques

MORE COMPLEX SEMANTICS Modalities Temporal interpretation