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Temporal Reasoning in Natural Language Processing

Temporal Reasoning in Natural Language Processing. Andrew Gibbs November 16 th , 2004. “The central component of any knowledge representation that supports Natural Language is the treatment of verbs and time .” – James Allen. Classifying Temporal Expressions. Types of Time. Time Point

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Temporal Reasoning in Natural Language Processing

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  1. Temporal Reasoning in Natural Language Processing Andrew Gibbs November 16th, 2004

  2. “The central component of any knowledge representation that supports Natural Language is the treatment of verbs and time.” – James Allen

  3. Classifying Temporal Expressions

  4. Types of Time • Time Point • Instantaneous point assignment with some transition in the world • e.g. light turning on, someone finding a pen • Interval • Extended stretch over which some event occurs • e.g. “John drove his car to work at 5pm.” • Duration • All intervals have durations • e.g. five minutes long • Points cannot have durations

  5. Interpreting Points and Intervals of Time T1 < T2 • point/interval T1 occurs before point/interval T2 T1 : T2 • interval T1 meets interval T2, or point T1 defines the beginning of interval T2, or point T2 defines the end of interval T1 T1 ⊆ T2 • point/interval T1 is contained in interval T2

  6. Temporal Sentence Classes • Stative Propositions • Describes a state. • Lacks defined ending point. • E.g. “Jack is happy.” • Activity Propositions • Describes an ongoing activity. • Occurs over an interval of time. • E.g. “Jack is running.” • Telic Propositions • Describes something that is brought to completion. • Achievement • E.g. “Jack recognized the man.” • Accomplishment • E.g. “They climbed the mountain.”

  7. Parsing Text for Temporal Expressions

  8. Markers for Time • Noun/Noun Phrase/Proper Noun • “day”, “Friday night”, “Wednesday” • Prepositional Phrase • “in a week” • Adjective • “current”, “future” • Adverb • “recently”, “hourly” • Adjective/Adverb Phrase • “two weeks ago”, “nearly half an hour ago” • Number • 3 (as in “He arrived at 3.”) • Subordinate Clauses • “…when the market stabilized”

  9. Examples of Current Methods • Logics • Tense Logic • Interval-based Temporal Logic • TIMEX2 • TimeML • DAML Ontology of time

  10. Tense Logic S – the time of speech E – the time of the event/state R – the reference time

  11. Tense Logic Jack sings. simple present: S=R, E=R Jack sang. simple past: R<S, E=R Jack will sing. simple future: S<R, E=R Jack has sung. present perfect: S=R, E<R Jack had sung. past perfect: R<S, E<R Jack will have sung. future perfect: S<R, E<R S – the time of speech E – the time of the event/state R – the reference time

  12. Tense Logic Jack is going to sing. posterior present: S=R, R<E Jack was going to sing. posterior past: R<S, R<E Jack will be going to sing. posterior future: S<R, R<E S – the time of speech E – the time of the event/state R – the reference time

  13. Interval-based Temporal Logic • Only based on intervals. • 13 basic binary relations between time intervals: before, after, overlaps, overlapped by, starts, started by, finishes, finished by, during, contains, meets, met by, equal to • Incomplete temporal information common in natural-language is captured by a disjunction of several of these relations.

  14. Interval-based Temporal Logic • Properties hold over every subinterval of an interval. Thus, the meaning of Holds(p,T) is that property p holds over interval T. ׂ • John was sleeping during the night. • Events hold only overa whole interval and not over any subinterval of it. Thus, Occurs(e,T) denotes that event e occurred at time T.ׂ • John broke his leg on Saturday at 6 P.M. • Processes hold over some subintervals of the interval in which they occur. Thus, Occurring(p,T) denotes the process p is occurring during time T. • John is walking around the block.

  15. TIMEX2 • Developed by DARPA Translingual Information Detection, Extraction, and Summarization (TIDES) in 2001 • Automatically annotates sentences with tags describing temporal information • Focuses on temporal markers (key words)

  16. TIMEX2 Tag Attributes

  17. TIMEX2 Examples • I was sick yesterday. • I was sick <TIMEX2 VAL=“2004-11-15”> yesterday </TIMEX2>. • Two years ago, the dance club drew about 100 students each week. • <TIMEX2 VAL=“2002”> Two years ago </TIMEX2>,the dance club drew about 100 students <TIMEX2 VAL=“2002” SET=“YES” GRANULARITY=“G1W” PERIODICITY=“F1W”> each week </TIMEX2>. • A major earthquake struck Los Angeles three years ago today. • A major earthquake struck Los Angeles <TIMEX2 VAL= “2001-11-16”> three years ago <TIMEX2 VAL=“2004-11-16”> today </TIMEX2> </TIMEX2>.

  18. TIMEX2 Point vs Duration Point in Time: He was happy five days ago. He was happy <TIMEX2 VAL=“2004-11-11”> five days ago</TIMEX2>. Duration: He was happy for five days. He was happy for <TIMEX2 VAL=“P5D”> five days </TIMEX2>.

  19. TimeML • Developed over a six-month period, funded by ARDA. • Automatically annotates sentences with tags describing temporal and event information. • Focuses on content rather than key words.

  20. TimeML • Extends the TIMEX2 annotation of attributes • Reasons with contextually underspecified temporal expressions: last week, in recent years • Identifies signals determining interpretation of temporal expressions • Temporal Prepositions: for, during, on, at • Temporal Connectives: before, after, while

  21. TimeML • Identifies all classes of event expressions • Tensed verbs: has left, was captured, will resign • Stative adjectives and other modifiers: sunken, stalled • Event nominal: merger, Military Operation, Gulf War • Creates dependencies between events and times • Anchoring: John left on Monday. • Orderings: The party happened after midnight. • Embedding: John said Mary Left.

  22. TimeML Example 1 John left two days before the attack. John <EVENT eid=“e1” class=“OCCURRENCE” tense=“PAST” aspect=“PERFECTIVE”> left </EVENT> <MAKEINSTANCE eiid=“ei1” eventID=“e1”/> <TIMEX3 tid=“t1” type=“DURATION” value=“P2D” temporalFunction=“false”> 2 days </TIMEX3> <SIGNAL sid=“s1”> before </SIGNAL> the <EVENT eid=“e2” class=“OCCURRENCE” tense=“NONE” aspect=“NONE> attack </EVENT> <MAKEINSTANCE eiid=“ei2” eventID=“e2/>

  23. TimeML Example 2 Bill wants to teach on Monday. Bill <EVENT eid=“e1” class=“I_STATE” tense=“PRESENT” aspect=“NONE> wants </EVENT> <MAKEINSTANCE eiid=“ei1” eventID=“e1”/> <SLINK eventInstanceID=“ei1” signalID=“s1” subordinateEvent=“e2” relType=“MODAL”/> <SIGNAL sid=“s1”> to </SIGNAL> <EVENT eid=“e2” class=“OCCURRENCE” tense=“NONE” aspect=“NONE”> teach </EVENT> <MAKEINSTANCE eiid=“ei2” eventID=“e2”/> <SIGNAL sid=“s2”> on </SIGNAL> <TIMEX3 tid=“t1” type=“DATE” temporalFunction=“true” value=“XXXX-WXX-1”> Monday </TIMEX3> <TLINK eventInstance=“ei2” relatedToTime=“t1” relType=“IS_INCLUDED”/>

  24. DAML Ontology of Time • Funded by DARPA • Still under development • Built with the intention of creating more accurate search engines. • Parses natural language in web-pages to determine the content. • Built-in facilities for fast temporal reasoning. • Based upon Interval-based Temporal logic. • Integrated with TimeML for the annotation of text.

  25. DAML example axiom <axiom id=“2.2-1”> before(T1, T2) && before(T2, T3) --> before(T1, T3) </axiom>

  26. Summary of DAML-Time/TimeML TimeML Annotation of Text ↓ Algorithms for Automatic TimeML annotation of text ↓ Interpret annotations in DAML-Time ↓ Reason in DAML-Time to match requests with services

  27. Example Query • I want the latest book by John McCarthy by next Tuesday. Author: John McCarthy Book: Formalizing Common Sense Date: 1998 Price: $24.95 Author: John McCarthy Book: LISP 1.5 Date: 1968 Price: $16.95

  28. Example Query • I want the latest book by John McCarthy by next Monday. Author: John McCarthy Ships within 5 days. Book: Formalizing Common Sense Date: 1998 Price: $24.95 Author: John McCarthy Book: LISP 1.5 Date: 1968 Price: $16.95

  29. References • Allen, James F., Natural Language Understanding, The Benjamin/Cummings Publishing Company, Menlo Park, California, (Addison-Wesley Publishing Company, Reading, Massachusetts), 1995 Pages 406-410 • B. Han and A. Lavie. A Framework for Resolution of Time in Natural Language. TALIP Special Issue on Spatial and Temporal Information Processing, 2004 http://www-2.cs.cmu.edu/~alavie/papers/BenH-TALIP-04.pdf • Kannan, A. Geetha, TV. Temporal Reasoning with Intelligent Databases. Anna University 2000 http://www.ncst.ernet.in/kbcs/vivek/issues/11.4/kannan/kannan.html • Galton, Anthony. Temporal Logic. Stanford Encyclopedia of Philosophy, 2003 http://plato.stanford.edu/entries/logic-temporal/ • Ligozat, Gerard. Representation of Space and Time. http://cslu.cse.ogi.edu/HLTsurvey/ch9node4.html

  30. References (cont’d) • Pustejovsky, J., J. Castano, R. Ingria, R. Saurí, R. Gaizauskas, A. Setzer, G. Katz (2003) TimeML: A Specification Language for Temporal and Event Expressions. In IWCS, International Workshop of Computational Semantics. Kluwer Academic Publishers. • Hobbs, Jerry R., Ferguson, G., Allen, J., Hayes, P., Niles, I., and Pease, A. 2002 A DAML Ontology of Time. http://www.cs.rochester.edu/~ferguson/daml/ • Ferro, Lisa. Instructional Manual of the Annotation of Temporal Expressions. MITRE, 2003 http://www.mitre.org/work/tech_papers/tech_papers_04/ferro_tides/ferro_tides.pdf • Shahar, Yuval. Temporal Reasoning in Clinical Domains. 1994 http://www.ise.bgu.ac.il/courses/trp/Shahar-1994.chapter3.doc • Hobbs, Jerry R. Ontologies for the Semantic Web: Time and Space. 2003 http://www.racai.ro/EUROLAN-2003/html/presentations/JerryHobbs/1

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