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Event Ordering using TERSEO system

Research Group on Language Processing and Information Systems. g PLSI. Event Ordering using TERSEO system. Estela Saquete Boró, Rafael Muñoz, Patricio Martinez-Barco. Departamento de Lenguajes y Sistemas Informáticos. Index. g PLSI. Introduction 2. Previous work

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Event Ordering using TERSEO system

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  1. Research Group on Language Processing and Information Systems g PLSI Event Ordering using TERSEO system Estela Saquete Boró, Rafael Muñoz, Patricio Martinez-Barco Departamento de Lenguajes y Sistemas Informáticos

  2. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  3. Introduction g PLSI • Automatic processes to extract relevant information • Event ordering using dates and time • Identification of temporal expressions • Resolution of temporal expression • Chronological order Research Group on Language Processing and Information Systems NLDB 2004

  4. Introduction g PLSI • Example: • “Today is July the 3rd (2003). • Tomorrow is my birthday” • Anaphoric expression: “Tomorrow” • Antecedent: July the 3rd (2003) • Referent: 07/04/2003 Research Group on Language Processing and Information Systems NLDB 2004

  5. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  6. Previous work g PLSI • Types of systems: • Based on Machine Learning: A supervised annotated corpus needed to automatically generate the system rules (percentage of appearance). • High precision results with concrete domains • Not very flexible, large annotated corpus • Based on knowledge: Previous knowledge base with rules to solve temporal expressions. • Greater flexibility • Our system based on Spanish knowledge, but this knowledge is automatically extended using automatic acquisition of rules for new languages Research Group on Language Processing and Information Systems NLDB 2004

  7. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  8. Temporal Expression Detection Temporal Signal Detection Graphic representation g PLSI Document TEMPORAL INFORMATION DETECTION TEMPORAL SIGNALS TEMPORAL EXPRESSIONS TEMPORAL EXPRESSION COREFERENCE RESOLUTION DATE ESTIMATION ORDERING KEY OBTAINING Research Group on Language Processing and Information Systems Dictionary ORDERING KEYS T.E. TAGS EVENT ORDERING ORDEREDTEXT NLDB 2004

  9. Description of the Event Ordering system g PLSI • Detection of temporal information: • Temporal Expression Detection Unit • Temporal Signal Detection Unit • Temporal expressions are resolved by the Temporal Expression Coreference Resolution unit that generates the XML tags. • Ordering key is obtained by the Ordering Key unit • With all this information, the Event Ordering Unit orders the text. Research Group on Language Processing and Information Systems NLDB 2004

  10. Description of the Event Ordering system g PLSI • Detection of temporal information: • Temporal Expression Detection Unit • Temporal Signal Detection Unit • Both share a common pre-processing of texts. Text are tagged with lexical and morphological information by a PosTagger and this information is the input of a temporal parser. • The temporal parser is implemented using and ascending technique and it is based on a temporal grammar. Research Group on Language Processing and Information Systems NLDB 2004

  11. Temporal Expression Detection g PLSI • One of the main tasks involved in trying to recognize and resolve temporal expressions is to classify them. A taxonomy with two different classification of the temporal expressions has been established: • Classification of the expression based on the kind of reference • Classification by the representation of the temporal value of the expression Research Group on Language Processing and Information Systems NLDB 2004

  12. Taxonomy of TE´s g PLSI • Classification of the expression based on the kind of reference: • Explicit Temporal Expressions: • Complete dates with or without time exp:01/01/2003 • Dates of events: Christmas • Implicit Temporal Expressions: • Exp. that refer to the Document Date: yesterday • Exp. that refer to another Date: a month later Research Group on Language Processing and Information Systems NLDB 2004

  13. Taxonomy of TE´s g PLSI • Classification by the representation of the temporary value of the expression: • Concrete. Give back a concrete day or/and time • Period. Give back a time interval. • Fuzzy. Give back approximate time interval. • Fuzzy concrete: a day of the last week • Fuzzy period: some months before Research Group on Language Processing and Information Systems NLDB 2004

  14. Temporal Signal Detection g PLSI • Temporal signals: • Relate the different events in texts • Establish a chronological order between these events. • Some examples of Temporal signals: • After • Before • During • When • Previously • While • At the time of Research Group on Language Processing and Information Systems NLDB 2004

  15. Description of the Event Ordering system g PLSI • Temporal Expression Coreference Resolution: • Anaphoric relation resolution based on a temporal model • Tagging of Temporal Expressions Research Group on Language Processing and Information Systems NLDB 2004

  16. Anaphoric Relation Resolution g PLSI • Looking for antecedents. • Two main candidates: • Newspaper´s date (DateP), • Date named before in the text (DateAnt). • Proccess: • By default, the newspaper´s date is used as a base referent if it exists. • If a non-anaphoric TE is found, this is stored as DateAnt. Research Group on Language Processing and Information Systems NLDB 2004

  17. Anaphoric Relation Resolution g PLSI Research Group on Language Processing and Information Systems NLDB 2004

  18. Tagging of TEs g PLSI • Set of XML tags (eXtensible Markup Language). Targets: • Showing the results of our system • Standarise the date-time formats of Internet texts. Research Group on Language Processing and Information Systems NLDB 2004

  19. Tagging of TEs g PLSI • Set of XML tags (eXtensible Markup Language). • Explicit Dates • < DATE_TIME ID =”value” • TYPE=”value” • VALDATE1=”value” • VALTIME1=”value” • VALDATE2=”value” • VALTIME2=”value” > • Expression • </DATE_TIME> Research Group on Language Processing and Information Systems NLDB 2004

  20. Tagging of TEs g PLSI • Implicit dates • < DATE_TIME_REF ID =”value” TYPE=”value” • VALDATE1=”value” • VALTIME1=”value” • VALDATE2=”value” • VALTIME2=”value” > • Expression • </DATE_TIME> Research Group on Language Processing and Information Systems NLDB 2004

  21. Ordering Keys Obtaining g PLSI • The study of the corpus revealed a set of temporal signals. • Each temporal signal denotes a relationship between the dates of the events that it is relating. • Example: in EV1 S EV2, the signal S denotes a relationship between EV1 and EV2. Assuming that F1 is the date of EV1 and F2 the date of EV2, S establish an order between EV1 and EV2. Research Group on Language Processing and Information Systems NLDB 2004

  22. Ordering Keys Obtaining g PLSI Research Group on Language Processing and Information Systems NLDB 2004

  23. Event ordering method g PLSI • Building of a table with the complete information from the XML tags • This table includes the columns ID, VALDATE1, VALTIME1, VALDATE2, VALTIME2 and VALORDER. • Ordering rules: • EV1 is previous to EV2, if the range associated with TE1 is prior to and not overlapping the range associated with TE2 or the ordering key is EV1<EV2 • EV1 is concurrent to EV2, if the range associated with TE1 overlaps the range associated with TE2 or the ordering key is EV1=EV2 Research Group on Language Processing and Information Systems NLDB 2004

  24. System example g PLSI In December 1, the French bathyscaphe Nautilus arrives at the Galician coast, previously there were some cracks. Text TEMPORAL INFORMATION DETECTION TEMPORAL SIGNAL: previously TEMPORAL EXPRESSION: In December 1 Research Group on Language Processing and Information Systems TEMPORAL EXPRESSION COREFERENCE RESOLUTION ORDERING KEY OBTAINING T.E. TAG: <DATE_TIME_REF VALDATE1=“12/01/2002”>in December 1 </DATE_TIME_REF> ORDERING KEY: event 1 > event 2 EVENT ORDERING NLDB 2004

  25. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  26. Application of Event Ordering in NLP tasks g PLSI • Applied in different tasks: • Summarization • Question Answering • Etc. • Temporal Question Answering can help current QA system to answer complex questions. Complex questions consist of two or more events related with a temporal signal, which establish the order between them. Research Group on Language Processing and Information Systems NLDB 2004

  27. Application in Question Answering g PLSI • Possible questions: • When did Iraq invade Kuwait? • When is the next New Hampshire Democratic primary? • Which US ship was attacked by Israeli forces during the Six Day war in the sixties? • Where did Bill Clinton study before going to Oxford University? Research Group on Language Processing and Information Systems NLDB 2004

  28. Complex Question Complex Answer INTERFACE TEMPORAL Q. A. PROCESSING SCRIPT Q. A. PROCESSING TEMPLATE Q. A. PROCESSING . . . . Simple Questions Simple Answers GENERAL PURPOSE QUESTION ANSWERING SYSTEM Application in Question Answering g PLSI Multilayered Question Answering Architecture Research Group on Language Processing and Information Systems NLDB 2004

  29. Example of Application in Question Answering g PLSI • Question: “Where did Bill Clinton study before going to Oxford University? • First of all, the unit recognizes the temporal signal, which in this case is “before” • Secondly, the complex question is divided: • Q1: Where did Bill Clinton study? • Q2: When did Bill Clinton go to Oxford University? Research Group on Language Processing and Information Systems NLDB 2004

  30. Example of Application in Question Answering g PLSI • Answers Q1: • Georgetown University (1964-1968) • Oxford University (1968-1970) • Yale Law School (1970-1973) • Answers Q2: • 1968 • Only Georgetown University fulfill the temporal constrainst, so that is the answer to the complex question. Research Group on Language Processing and Information Systems NLDB 2004

  31. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  32. System evaluation g PLSI • Corpus Spanish: Training (50 articles) and Test (50 articles) • Kappa factor: measures the affinity in agreement between a set of annotators when they make categories judgments k=0.953 • Two measures • Precision: Num Successes / Num Treated Ref • Recall: Num Successes / Num Real Ref Research Group on Language Processing and Information Systems NLDB 2004

  33. System evaluation g PLSI • The establishment of a correct order between the events implies that the resolution is correct and the events are placed on a timeline. For this reason, an evaluation of the resolution of Temporal Expressions has been made. Research Group on Language Processing and Information Systems EVENTS AND ITS TEMPORAL EXPRESSIONS • EVENT 1: Jan. 1, 1967 • EVENT 2: a year later • EVENT 3: two months before EV1 EV3 EV2 01/01/1967 10/01/1967 01/01/1968 NLDB 2004

  34. System evaluation g PLSI Research Group on Language Processing and Information Systems NLDB 2004

  35. System evaluation g PLSI • Expressions like “el sábado hubo cinco accidentes” (Saturday there were five accidents) need context information of the sentence where the reference is, in this case, the time of the sentence´s verb. Our system does not use this information. • There is not a world knowledge database, for instance: “two days before the Iraqi war”. We don´t have this information nowadays. Research Group on Language Processing and Information Systems NLDB 2004

  36. Index g PLSI • Introduction • 2. Previous work • 3. Description of the Event Ordering System • 4. Application of Event Ordering in NLP tasks • 5. System evaluation • 6. Conclusions Research Group on Language Processing and Information Systems NLDB 2004

  37. Conclusions g PLSI • Obtaining facts related to an event from a Documental Database  Chronology. • System: • Title of the news linked to the date of the documents • Recognition of temporal expressions. Events  sentences with TE • Module for treating TE is applied • The ordering module tags the order of the events in the text Research Group on Language Processing and Information Systems NLDB 2004

  38. Conclusions g PLSI • Application in Temporal Question Answering: Decomposition of complex temporal questions in simple ones. • Future work: • Cope with context information and world knowledge • Multilingual evaluation of the system Research Group on Language Processing and Information Systems NLDB 2004

  39. Research Group on Language Processing and Information Systems g PLSI Event Ordering using TERSEO system Estela Saquete Boró, Rafael Muñoz, Patricio Martinez-Barco Departamento de Lenguajes y Sistemas Informáticos

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