1. Temporal Reasoning in Natural Language Processing Andrew Gibbs November 16 th, 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 Time Point Instantaneous point assignment with some transition in the world Instantaneous point assignment with some transition in the world e.g. light turning on, someone finding a pen e.g. light turning on, someone finding a pen Interval Interval Extended stretch over which some event occurs Extended stretch over which some event occurs e.g. “John drove his car to work at 5pm.” e.g. “John drove his car to work at 5pm.” Duration Duration All intervals have durations All intervals have durations e.g. five minutes long e.g. five minutes long Points cannot have durations Points cannot have durations

5. Interpreting Points and Intervals of Time T1 < T2 point/interval T1 occurs before point/interval 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 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 point/interval T1 is contained in interval T2

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

7. Aspectual Class Can Be True at a point? Can Be True during an Interval Temporal Modifier in Stative Phrase YesYesNo ActivityNoYesNo AchievementYesNoYes AccomplishmentNoYesYes

8. Parsing Text for Temporal Expressions

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

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

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

12. 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

13. 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

14. Interval-based Temporal Logic Only based on intervals. 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 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. Incomplete temporal information common in natural-language is captured by a disjunction of several of these relations.

16. Interval-based Temporal Logic 1.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. John was sleeping during the night. 2.Events hold only over a 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. John broke his leg on Saturday at 6 P.M. 3.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. John is walking around the block.

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

18. TIMEX2 Tag Attributes AttributeFunctionExample VAL Contains normalized form of the date/time. VAL=“1964-10-16” MOD Captures temporal modifiers. MOD=“APPROX” SET Identifies expressions denoting sets of times. SET=“YES” PERIODICITY Captures the period between regularly recurring times. PERIODICITY=“PIM” GRANULARITY Captures the unit of time denoted by each set member in a set of times. GRANULARITY=“G3D” NON_SPECIFIC Identifies non-specific expressions. NON_SPECIFIC=“YES” COMMENT Contains any comments the annotator wants to add. COMMENT=“context garbled”

19. TIMEX2 Examples I was sick yesterday. I was sick yesterday. Two years ago, the dance club drew about 100 students each week. Two years ago, the dance club drew about 100 students each week. A major earthquake struck Los Angeles three years ago today. A major earthquake struck Los Angeles three years ago today.

20. TIMEX2 Point vs Duration Point in Time: He was happy five days ago. Duration: He was happy for five days.

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

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

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

24. TimeML Example 1 John left two days before the attack. John left</EVENT> 2 days</TIMEX3> before</SIGNAL> the attack</EVENT>

25. TimeML Example 2 Bill wants to teach on Monday.Bill wants</EVENT> <SLINK eventInstanceID=“ei1” signalID=“s1” subordinateEvent=“e2” relType=“MODAL”/> to</SIGNAL> teach</EVENT> on</SIGNAL> Monday</TIMEX3>

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

27. DAML example axiom before(T1, T2) && before(T2, T3) --> before(T1, T3) </axiom>

28. 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

29. Example Query I want the latest book by John McCarthy by next Tuesday.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

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

31. 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 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 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.pdfhttp://www-2.cs.cmu.edu/~alavie/papers/BenH-TALIP- 04.pdfhttp://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 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 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/ Galton, Anthony. Temporal Logic. Stanford Encyclopedia of Philosophy, 2003 http://plato.stanford.edu/entries/logic-temporal/http://plato.stanford.edu/entries/logic-temporal/ Ligozat, Gerard. Representation of Space and Time. http://cslu.cse.ogi.edu/HLTsurvey/ch9node4.html Ligozat, Gerard. Representation of Space and Time. http://cslu.cse.ogi.edu/HLTsurvey/ch9node4.html

32. 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. 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/ 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/ 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 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 http://www.mitre.org/work/tech_papers/tech_papers_04/ferro_tides/ferro_ tides.pdf 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 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 Hobbs, Jerry R. Ontologies for the Semantic Web: Time and Space. 2003 http://www.racai.ro/EUROLAN- 2003/html/presentations/JerryHobbs/1