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1 Temporal Information Extraction Inderjeet Mani

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1 1 Temporal Information Extraction Inderjeet Mani

2 2 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

3 3 Motivation: Question- Answering When is Ramadan this year? What was the largest U.S. military operation since Vietnam? Tell me the best time of the year to go cherry- picking. How often do you feed a pet gerbil? Is Gates currently CEO of Microsoft? Did the Enron merger with Dynegy take place? How long did the hostage situation in Beirut last? What is the current unemployment rate? How many Iraqi civilian casualties were there in the first week of the U.S. invasion of Iraq? Who was Secretary of Defense during the Gulf War?

4 4 Motivation: Coherent and Faithful Summaries Single-document sentence extraction summarizers are plagued by dangling references –especially temporal ones Multi-Document summarizers can be misled by the weakness of vocabulary overlap methods –leads to inappropriate merging of distinct events.. worked in recent summers....was the source of the virus last week.. …where Morris was a computer science undergraduate until June.. …..whose virus program three years ago disrupted…

5 5 An Example Story Feb. 18, 2004 Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her run twist ankle during finishes before push before during 1. When did the running occur? Yesterday. 2. When did the twisting occur? Yesterday, during the running. 3. Did the pushing occur before the twisting? Yes. 4. Did Holly keep running after twisting her ankle? 5. Probably not.

6 6 Temporal Information Extraction Problem Input: A natural language discourse Output: representation of events and their temporal relations Feb. 18, 2004 Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her.

7 7 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply Idea for Temporal IE: Make progress by focusing on a particular top-down slice (i.e., time), using its rich structure

8 8 Theories EventsTime Language AI & logic Formal Linguistics

9 9 Linguistic Theories Events –Event Structure (event subclasses and parts) –Tense (indicates location of event in time, via verb inflections, modals, auxiliaries, etc.) –Grammatical Aspect (indicates whether event is ongoing, finished, completed) Time Adverbials Relations between events and/or times –temporal relations –we will also need discourse relations

10 10 Tense All languages that have tense (in the semantic sense of locating events in time) can express location in time Location can be expressed relative to a deictic center that is the current ‘moment’ of speech, or ‘speech time’, or ‘speech point’ –e.g., tomorrow, yesterday, etc. Languages can also express temporal locations relative to a coordinate system –a calendar, e.g., 1991 (A.D.), – a cyclically occurring event, e.g., morning, spring, – an arbitrary event, e.g., the day after he married her. A language may have tense in the above semantic sense, without expressing it using tense morphemes –Instead, aspectual morphemes and/or modals and auxiliaries may be used.

11 11 Mandarin Chinese Has semantic tense Lacks tense morphemes Instead, it uses ‘aspect’ markers to indicate whether an event is ongoing (-zhai, -le), completed (-wan), terminated (-le, - guo), or in a result state (-zhe) –But aspect markers are often absent 我 看 电视 wo kan dianshi* I watch / will watch / watched TV *Example from Congmin Min, MS Thesis, Georgetown, 2005.

12 12 Burmese* No semantic tense, but all languages that lack semantic tense all have a realis/irrealis distinction. Events that are ongoing or that were observed in the past are expressed by sentence-final realis particles –te, -tha, - ta, and –hta. For unreal or hypothetical events (including future and present and hypothetical past events), the sentence-final irrealis particles –me, -ma, and –hma are used. *Comrie, B. Tense. Cambridge, 1985.

13 13 Tense as Anaphor: Reichenbach A formal method for representing tense, based on which one can locate events in time Tensed utterances introduce references to 3 ‘time points’ –Speech Time: S –Event Time: E –Reference Time: R S I had [mailed the letter] E [when John came & told me the news] R E < R < S Three temporal relations are defined on these time points –at, before, after 13 different relations are possible N.B. the concept of ‘time point’ is an abstraction –- it can map to an interval ER S time

14 14 Reichenbachian Tense Analysis Tense is determined by relation between R and S –R=S, R S Aspect is determined by relation between E and R –E=R, E R Relation of E relative to S not crucial –Represent R R I ate a peach E S E>R

15 15 G  : It is always going to be the case that . H  : It always has been the case that . F  : It will be at some point in the future be the case that . P  : It was at some point in the past the case that . F  = ¬G¬  P  = ¬H¬  System K t : (a)   H F  : What is, has always been going to be; (b   G P  : What is, will always have been; (c) H(   )  (H   H  ): Whatever always follows from what always has been, always has been; (d) G(   )  (G   G  ): Whatever always follows from what always will be, always will be. Priorean Tense Logic

16 16 Tense as Operator: Prior Free iteration captures many more tenses, –I would have slept PFP  But also expresses many non-NL tenses –PPPP  [It was the case] 4 John had slept

17 17 Event Classes (Lexical Aspect) STATIVES know, sit, be clever, be happy, killing, accident –can refer to state itself (ingressive) John knows, or to entry into a state (inceptive) John realizes – *John is knowing Bill, *Know the answer, *What John did was know the answer ACTIVITIES walk, run, talk, march, paint –if it occurs in period t, a part of it (the same activity) must occur for most sub-periods of t –X is Ving entails that X has Ved –John ran for an hour,*John ran in an hour ACCOMPLISHMENTS build, cook, destroy –culminate (telic) –X is Ving does not entail that X has Ved. –John booked a flight in an hour, John stopped building a house ACHIEVEMENTS notice, win, blink, find, reach –instantaneous accomplishments –*John dies for an hour, *John wins for an hour, *John stopped reaching New York

18 18 Aspectual Composition Expressions of one class can be transformed into one of another class by combining with another expression. –e.g., an activity can be changed into an accomplishment by adding an adverbial phrase expressing temporal or spatial extent –I walked (activity) –I walked to the station / a mile / home (accomplishment) –I built my house (accomplishment). –I built my house for an hour (activity). Moens & Steedman (1988) – implement aspectual composition in a transition network

19 19 Example: Classifying Question Verbs Androutsopoulos’s (2002) NLITDB system allows users to pose temporal questions in English to an airport database that uses a temporal extension of SQL Verbs in single-clause questions with non-future meanings are treated as states –Does any tank contain oil? Some verbs may be ambiguous between a (habitual) state and an accomplishment –Which flight lands on runway 2? –Does flight BA737 land on runway 2 this afternoon Activities are distinguished using the imperfective paradox: –Were any flights taxiing? implies that they taxied –Were any flights taxiing to gate 2? does not imply that they taxied. So, taxi will be given –an activity verb sense, one that doesn’t expect a destination argument, and –an accomplishment verb sense, one that expects a destination argument.

20 20 Grammatical Aspect Perfective – focus on situation as a whole –John built a house Imperfective – focus on internal phases of situation –John was building a house built.a.h was building.a.h EnglishVerbal tense and aspect morphemes, e.g., for present and past perfect FrenchTense (passé composé) Mandarinmorphemes –le and –guo Englishprogressive verbal inflection -ing FrenchTense (imparfait) Mandarinprogressive morpheme –zai and resultative morpheme –zhe.

21 21 Inferring Temporal Relations 1.Yesterday Holly was running a marathon when she twisted her ankle. FINISHES David had pushed her. BEFORE 2.I had mailed the letter when John came & told me the news AFTER 3.Simpson made the call at 3. Later, he was spotted driving towards Westwood. AFTER 4.Max entered the room. Mary stood up/was seated on the desk. AFTER/OVERLAP 5.Max stood up. John greeted him. AFTER 6.Max fell. John pushed him. BEFORE 7.Boutros-Ghali Sunday opened a meeting in Nairobi of....He arrived in Nairobi from South Africa BEFORE 8.John bought Mary some flowers. He picked out three red roses. DURING

22 22 Linguistic Information Needed for Temporal IE Events Tense Aspect Time adverbials Explicit temporal signals (before, since, at, etc.) Discourse Modeling –For disambiguation of time expressions based on context –For tracking sequences of events (tense/aspect shifts) –For computing Discourse Relations Commonsense Knowledge –For inferring Discourse Relations –For inferring event durations

23 23 Narrative Ordering Temporal Discourse Interpretation Principle (Dowty 1979) –Reference time for the current sentence is a time consistent with its time adverbials if any, or else it immediately follows reference time of the previous sentence. –The overlap of statives is a pragmatic inference,(hinting at a theory of defaults) A man entered the White Hart. He was wearing a black jacket. Bill served him a beer. Discourse Representation Theory (Kamp and Reyle 1993) –In successive past tense sentences which lack temporal adverbials, events advance the narrative forward, while states do not. –Overlapping statives come out of semantic inference rules Neither theory explicitly represents discourse relations, though they are needed (e.g., 6-8 above)

24 24 Discourse Representation Theory (example) A man entered the White Hart. He was wearing a black jacket. Bill served him a beer. Rpt  {} e1, t1, x, y enter(e1, x, y), man(x), y= theWhiteHart t1 < n, e1  t1 Rpt  e e2, t2, x1, y1 PROG(wear(e2, x1, y1)), black-jacket(y1), x1=x t2 < n, e2 ο t2, e1  e e3, t3, x2, y2, z serve(e3, x2, y2, z), beer(z), x2=Bill, y2=x t3 < n, e3  t3 Rpt  e3 e1 < e3

25 25 Overriding Defaults Lascarides and Asher (1993)*: temporal ordering is derived entirely from discourse relations (that link together DRS’s, based on SDRT formalism). Example –Max switched off the light. The room was pitch dark. –Default inference: OVERLAP –Use an inference rule that if the room is dark and the light was just switched off, the switching off caused the room to become dark. –Inference: AFTER Problem: requires large doses of world knowledge *L&P 1993

26 26 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

27 27 Time and Events in Logic Events Time Events Time Events Instants Intervals Instants Intervals Instants

28 28 Instant Ontology Consider the event of John’s reading the book Decompose into an infinite set of infinitesimal instants Let T be a set of temporal instants. Let < (BEFORE) be a temporal ordering relation between instants Properties: irreflexive, antisymmetric, transitive, and complete –Antisymmetric => time has only one direction of movement –Irreflexive and Transitive => time is non-cyclical –Complete => < is a total ordering

29 29 Instants -- Problem Where Truth Values Change P = The race is on T-R = the time of running the race T-AR = the time after running the race R and AR have to meet somewhere If we choose instants, there is some instant x where T-R and AR meet Either we have P and not P both true at x, or there is a truth value gap at x This is called the Divided Instant Problem (D.I.P.) T-R T-AR Pnot P ? x

30 30 Ordering Relations on Intervals Unlike instants, where we have only <, we can have at least 3 ordering relations on intervals –Precedence < : I1 < I2 iff  t1  I1,  t2  I2, t1 < t2 (where < is defined over instants) –Temporal Overlap O : I1 O I2 iff I1  I2   –Temporal Inclusion  : I1  I2 iff I1  I2

31 31 Instants versus Intervals Instants –We understand the idea of truth at an instant –In cases of continuous change, e.g., a tossed ball, we need a notion of a durationless event in order to explain the trajectory of the ball just before it falls Intervals –We often conceive of time as broken up in terms of events which have a certain duration, rather than as a (infinite) sequence of durationless instants. –Many verbs do not describe instantaneous events., e.g., has read, ripened –Duration expressions like yesterday afternoon aren’t construed as instants

32 32 Allen’s Interval-Based Ontology* Instants are banished –So, avoids the divided instant problem Short duration intervals will be instant-like Uses 13 relations –Relations are mutually exclusive All 13 relations can be expressed using meet: –  X  Y [Before (X, Y)   Z [meet(X, Z) & meet(Z, Y)]] *James F. Allen, ‘Towards a General Theory of Action and Time’, Artificial Intelligence 23 (1984): 123–54.

33 33 Allen’s 13 Temporal Relations A B A B A B A B A B A B A B A FINISHES B B is FINISHED by A A is BEFORE B B is AFTER A A MEETS B B is MET by A A OVERLAPS B B is OVERLAPPED by A A STARTS B B is STARTED by A A is EQUAL to B A DURING B B CONTAINS A d, di m, mi s, si o, oi f, fi

34 34 <>ddiooimmissiffi <?>> oi mi d f > > > >>> d<>d? oi mi d f <>d d

35 35 Temporal Closure: Sputlink* in TANGO *Verhagen (2005)

36 36 AI Reasoning about Events Situation Calculus Holds(Have(John, book), t1) Holds(Have(Mary, book), t2) Holds(Have(Z, Y), Result(give(X, Y, Z), t)) –t-i are states Concurrent actions cannot be represented No duration of actions or delayed effects Event Calculus HoldsAt(Have(J, B), t1) HoldsAt(Have(M, B), t2) Terminates(e1, Have(J, B)) Initiates(e1, Have(M, B)) Happens(e, t) [t is a time point] Involves non-monotonic reasoning Handles frame problem using circumscription John gave a book to Mary

37 37 Temporal Question-Answering using IE + Event Calculus Mueller (2004)*: Takes instantiated MUC terrorist event templates and represents information in EC Adds commonsense knowledge about terrorist domain –e.g., if a bomb explodes, it’s no longer activated Commonsense knowledge includes frame axioms –e.g., if an object starts falling, then its height will be released from the commonsense law of inertia Example temporal questions – Was the car dealership damaged before the high-power bombs exploded? Ans: No. Requires reasoning that the damage did not occur at all times t prior to the explosion Problem: requires large doses of world knowledge *Mueller, Erik T. (2004). Understanding script-based stories using commonsense reasoning. Cognitive Systems Research, 5(4),

38 38 Temporal Question Answering using IE + Temporal Databases In NLITDB, semantic relation between a question event and the adverbial it combines with is inferred by a variety of inference rules. State + ‘point’ adverbial –Which flight was queueing for runway 2 at 5:00 pm?: state coerced to an achievement, viewed as holding at the time specified by the adverbial. Activity + point adverbial –can mean that the activity holds at that time, or that the activity starts at that time, e.g., Which flight queued for runway 2 at 5:00 pm? An accomplishment may indicate inception or termination –Which flight taxied to gate 4 at 5:00 pm? can mean the taxiing starts or ends at 5 pm.

39 39 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

40 40 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply

41 41 Events in NLP Topic: well-defined subject for searching –document- or collection-level Template: structure with slots for participant named entities –document-level Mention: linguistic expression that expresses an underlying event –phrase-level (verb/noun)

42 42 Event Characteristics Can have temporal a/o spatial locations Can have types –assassinations, bombings, joint ventures, etc. Can have members Can have parts Can have people a/o other objects as participants Can be hypothetical Can have not happened

43 43 MUC Event Templates Wall Street Journal, 06/15/88 MAXICARE HEALTH PLANS INC and UNIVERSAL HEALTH SERVICES INC have dissolved a joint venture which provided health services.

44 44 ACE Event Templates Four additional attributes for each event mention –Polarity (it did or did not occur) –Tense (past, present, future) –Modality (real vs. hypothetical) –Genericity (specific vs. generic) Argument slots (4 -7) specific to each event –E.g., Trial-Hearing event has slots for the Defendant, Prosecutor, Adjudicator, Crime, Time, and Place. TypeSubtype LifeBe-Born, Marry, Divorce, Injure, Die MovementTransport TransactionTransfer-Ownership, Transfer-Money BusinessStart-Org, Merge-Org, Declare-Bankruptcy, End-Org ConflictAttack, Demonstrate ContactMeet, Phone-Write PersonnelStart-Position, End-Position, Nominate, Elect JusticeArrest-Jail, Release-Parole, Trial-Hearing, Charge-Indict, Sue, Convict, Sentence, Fine, Execute, Extradite, Acquit, Appeal, Pardon From Lisa

45 45 Mention-Level Events Event expressions: –tensed verbs; has left, was captured, will resign; –stative adjectives; sunken, stalled, on board; –event nominals; merger, Military Operation, war; Dependencies between events and times: –Anchoring; John left on Monday. –Orderings; The party happened after midnight. –Embedding; John said Mary left.

46 46 TIMEX2 (TIDES/ACE) Annotation Scheme Time Points the third week of October Durations half an hour long Indexicality tomorrow He wrapped up a three-hour meeting with the Iraqi president in Baghdad today. Sets every Tuesday Fuzziness Summer of 1990 This morning early last night

47 47 TIMEX2 Inter-annotator Agreement Georgetown/MITRE (2001) –193 English docs,.79 F Extent,.86 F VAL –5 annotators –Annotators deviate from guidelines, and produce systematic errors (fatigue?) several years ago: PXY instead of PAST_REF all day: P1D instead of YYYY-MM-DD LDC (2004) –49 English docs,.85 F Extent,.80F VAL –19 Chinese docs,.83 Extent –2 annotators

48 48 Example of Annotator Difficulties (TERN 2004*) *Time Expression Recognition and Normalization Competition (timex2.mitre.org)

49 49 TIMEX2 – A Mature Standard Extensively debugged Detailed guidelines for English and Chinese Evaluated for English, Arabic, Chinese, Korean, Spanish, French, Swedish, and Hindi Applied to news, scheduling dialogues, other types of data Corpora available through ACE, MITRE

50 50 Temporal Relations in ACE Restricted to verbal events (verbs of scheduling, occurrence, aspect etc.) The event and the timex must be in the same sentence Eight temporal relations –Within The bombing occurred [during] the night. –Holds They were meeting [all] night. –Starting, Ending The talks [ended (on)] Monday. –Before, After The initial briefs have to be filed [by] 4 p.m. Tuesday ” –At-Beginning, At-End Sharon met with Bill [at the start] of the three-day conference From Lisa

51 51 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

52 52 TimeML Annotation Scheme A Proposed Metadata Standard for Markup of events, their temporal anchoring, and how they are related to each other Marks up mention-level events, time expressions, and links between events (and events and times) Developer: James Pustejovsky (& co.)

53 53 An Example Story Feb. 18, 2004 Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her run twist ankle during finishes before push before during 1. When did the running occur? Yesterday. 2. When did the twisting occur? Yesterday, during the running. 3. Did the pushing occur before the twisting? Yes. 4. Did Holly keep running after twisting her ankle? 5. Probably not.

54 54 An Attested Story AP-NR EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. Past

55 55 TimeML Events AP-NR EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq.

56 56 TimeML Event Classes Occurrence: –die, crash, build, merge, sell, take advantage of,.. State: –Be on board, kidnapped, recovering, love,.. Reporting: –Say, report, announce, I-Action: –Attempt, try,promise, offer I-State: –Believe, intend, want, … Aspectual: –begin, start, finish, stop, continue. Perception: –See, hear, watch, feel.

57 57 Temporal Anchoring Links AP-NR EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq.

58 58 TLINK Types Simultaneous (happening at the same time) Identical: (referring to the same event) John drove to Boston. During his drive he ate a donut. Before the other: In six of the cases suspects have already been arrested. Immediately before the other: All passengers died when the plane crashed into the mountain. Including the other: John arrived in Boston last Thursday. Exhaustively during the duration of the other: John taught for 20 minutes. Beginning of the other: John was in the gym between 6:00 p.m. and 7:00 p.m. Ending of the other: John was in the gym between 6:00 p.m. and 7:00 p.m.

59 59 TLINK Example John taught 20 minutes every Monday. John taught 20 minutes every Monday

60 60 Subordinated Links AP-NR EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq.

61 61 SLINK Types SLINK or Subordination Link is used for contexts introducing relations between two events, or an event and a signal, of the following sort: Modal: Relation introduced mostly by modal verbs (should, could, would, etc.) and events that introduce a reference to a possible world --mainly I_STATEs: John should have bought some wine. Mary wanted John to buy some wine. Factive: Certain verbs introduce an entailment (or presupposition) of the argument's veracity. They include forget in the tensed complement, regret, manage: John forgot that he was in Boston last year. Mary regrets that she didn't marry John. Counterfactive: The event introduces a presupposition about the non-veracity of its argument: forget (to), unable to (in past tense), prevent, cancel, avoid, decline, etc. John forgot to buy some wine. John prevented the divorce. Evidential: Evidential relations are introduced by REPORTING or PERCEPTION: John said he bought some wine. Mary saw John carrying only beer. Negative evidential: Introduced by REPORTING (and PERCEPTION?) events conveying negative polarity: John denied he bought only beer. Negative: Introduced only by negative particles (not, nor, neither, etc.), which will be marked as SIGNALs, with respect to the events they are modifying: John didn't forgot to buy some wine. John did not wanted to marry Mary.

62 62 Aspectual Links Th' U.S. military buildup in Saudi Arabia corntinued at fevah pace, wif Syrian troops now part of a multinashunal fo'ce camped out in th' desert t'guard the Saudi kin'dom fum enny noo threst by Iraq. In a letter to President Hashemi Rafsanjani of Iran, read by a broadcaster over Baghdad radio, Saddam said he will begin withdrawing troops from Iranian territory a week from tomorrow and release Iranian prisoners of war.

63 63 Towards TIMEX3 Decompose more –Smaller tag extents compared to TIMEX2 just days after another court dismissed other corruption charges against his father. –N. B. extent marking a source of inter-annotator disagreements in ACE TERN 2004 evaluation –Avoid tag Embedding two weeks from next Tuesday Include temporal functions for delayed evaluation –Allow non-consuming tags Put relationships in Links

64 64 TIMEX3 Annotation Time Points midnight tomorrow Durations two weeks from June 7, 2003 Sets three days every month twice a month

65 65 TimeML and DAML-Time Ontology* We ship e1 2 days t1 after the purchase e2 TimeML < TLINK eventInstanceID=e1 relatedToEventInstance=e2 relType=AFTER/> DAML-OWL atTime(e1, t1) & atTime(e2, t2) & after(t1, t2) & timeBetween(T, t1, t2) & duration(T, *Days*)=2 *Hobbs & Pustejovsky, in I. Mani et al., eds., The Language of Time

66 66 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

67 67 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply

68 68 Callisto Annotation Tool

69 69 Tabular Annotation of Links

70 70 TANGO Graphical Annotator

71 71 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

72 72 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply

73 73 Timex2/3 Extraction Accuracy –Best systems: TIMEX2: 95 F Extent,.8XF VAL (TERN* 2004 English) –GUTime:.85F Extent,.82F VAL (TERN 2004 training data English) –KTX:.87F Extent,.86F VAL (100 Korean documents) Machine Learning –Tagging Extent: easily trained –Normalizing Values: harder to train

74 74 TimeML Event Extraction Easier than MUC template events (those were.6F) Part-of-speech tagging to find verbs Lexical patterns to detect tense and lexical and grammatical aspect Syntactic rules to determine subordination relations Recognition and Disambiguation of event nominals, e.g., war, building, construction, etc. Evita (Brandeis): –0.8F on verbal events (overgenerates generic events which weren’t marked in TimeBank) –0.64F on event nominals (WordNet-derived, disambiguated via SemCor training)

75 75 TempEx in Qanda

76 76 Extracting Temporal Relations based on Tense Sequences Song & Cohen 1991: Adopt a Reichenbachian tense representation Use rules for permissible tense sequences –When the tense moves from simple present to simple past, the event time moves backward, and from simple present to simple future, it moves forward. –When the tense of two successive sentences is the same, they argue that the event time moves forward, except for statives and unbounded processes, which keep the same time. Won’t work in cases of discourse moves –When the tense moves from present perfect to simple past, or present prospective (John is going to run) to simple future, the event time of the second sentence is less than or equal to the event time of the first sentence. However, incorrectly rules out, among others, present tense to past perfect transitions. Song & Cohen: AAAI’91

77 77 Extracting Temporal Relations by Heuristic Rule Weighting Approach assigns weights to different ordering possibilities based on the knowledge sources involved. Temporal adverbials and discourse cues are first tried; if neither are present, then default rules based on tense and aspect are used. –Given a sentence describing past tense activity followed by one describing a past tense accomplishment or achievement, the second event can only occur just after the activity; it can’t precede, overlap, or be identical to it. If the ordering is still ambiguous at the end of this, semantic rules are used based on modeling the discourse in terms of threads. –Assumes there is one ‘thread’ that the discourse is currently following. a. John went into the florist shop. b. He had promised Mary some flowers. c. She said she wouldn’t forgive him if he forgot. d. So he picked out three red roses. Each utterance is associated with exactly one of two threads: –(i) going into the florist’s shop and –(ii) interacting with Mary. Prefer an utterance to continue a current thread which has the same tense or is semantically related to it –(i) would be continued by d. based on tense *Janet Hitzeman, Marc Moens, and Claire Grover, ‘Algorithms for Analysing the Temporal structure of Discourse’, EACL’1995, 253–60.

78 78 Heuristic Rules (Georgetown GTag) Uses 187 hand-coded rules –LHS: tests based on TimeML-related features and pos-tags –RHS: TimeML TLINK classes (~ 13 Allen) Ordered into Classes –R1&2: event anchored w/o signal to time in same clause –R3 (28): main clause event in 2 successive sentences –R4: reporting verb and document time –R5 (54): reporting verb and event in same sentence –R6 (87): events in same sentence –R7: timex linked to document time Rules can have confidence ruleNum=6-6 If sameSentence=YES && sentenceType=ANY && conjBetweenEvents=YES && arg1.class=EVENT && arg2.class=EVENT && arg1.tense=PAST && arg2.tense=PAST && arg1.aspect=NONE && arg2.aspect=NONE && arg1.pos=VB && arg2.pos=VB && arg1.firstVbEvent=ANY && arg2.firstVbEvent=ANY then infer relation=BEFORE Confidence = 1.0 Comment = “ they traveled far and slept the night in a rustic inn ”

79 79 Using Web-Mined Rules Lexical relations (capturing causal and other relations, etc.) –kill => die (always) –push => fall (sometimes: Max fell. John pushed him.) Idea: leverage the distributions found in large corpora VerbOcean: database from ISI that contains lexical relations mined from Google searches –E.g., X happens before Y, where X and Y are WordNet verbs highly associated in a corpus Converted to GUTenLink Format Yields 4199 rules! ruleNum= If arg1.class=EVENT && arg2.class=EVENT && arg1.word=learn && # uses morph normalization arg2.word=forget && then infer relation=BEFORE

80 80 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

81 81 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply

82 82 Related Machine Learning Work (Li et al. ACL’2004) obtained 78-88% accuracy on ordering within-sentence temporal relations in Chinese texts. (Mani et al., HLT’2003 short) obtained 80.2 F- measure training a decision tree on 2069 clauses in anchoring events to reference times that were inferred for each clause. (Lapata and Lascarides NAACL’2004) used found data to successfully learn which (possibly ambiguous) temporal markers connect a main and subordinate clause, without inferring underlying temporal relations.

83 83 Car Sim: Text to Accident Simulation System* Carries out TimeML annotation of Swedish accident reports Builds an event ordering graph using machine learning, with separate decision trees for local and global TLINKS Generates, based on domain knowledge, a simulation of the accident Anders Berglund. Extracting Temporal Information and Ordering Events for Swedish. MS Thesis. Lund University

84 84 Prior Machine Learning from TimeBank Mani (p.c., 2004): –TLINKs converted into feature vectors from TimeBank 1.0 tags –TLINK relType converted to feature vector class label, after collapsing – Accuracy of C5.0.1 decision rules:.55 F majority class Boguraev & Ando (IJCAI’2005): –Uses features based on local syntactic context (chunks and clause-structure) –trained a classifier for within-sentence TLINKS on Timebank 1.1:.53F Bottom Line: TimeBank corpus doesn’t provide enough data for training learners?

85 85 Insight: TLINK Annotation (Humans) Inter-annotator reliability is ~.55F –But agreement on LINK labels: 77% So, the problem is largely which events to link –Within sentence, adjacent sentences, across document? –Guidelines aren’t that helpful Conclusion: global TLINKing is too fatiguing –0.84 TLINKS/event in corpus

86 86 Temporal Reasoning to the Rescue Earlier experiments with SputLINK in TANGO (interactive, text-segmented closure) indicated that without closure, annotators cover 4% of all possible links. With closure, an annotator could cover about 65% of all possible links in a document. Of those links, 84% were derived by the algorithm Initial Links364% User Prompts10912% Derived Links77584%

87 87 IE Methodology Raw Corpus Annotated Corpus Initial Tagger Annotation Editor Annotation Guidelines Machine Learning Program Raw Corpus Learn- ed Rules Annotated Corpus Rule Apply Axioms

88 88 Temporal Closure as an Oversampling Method Closing the Corpus (with 745 axioms) – Number of TLINKs goes up > 11 times! –BEFORE links go up from 3170 Event-Event and 1229 Event-Time TLINKs to 68,585 Event-Event and 186,65 Event-Time TLINKs –Before closure: 0.84 TLINKs/event –After closure: 9.49 TLINKs/event Events, 2114 Times RelationEvent- Event Event- Time IBEFORE BEGINS ENDS SIMULTAN EOUS INCLUDES (65.3%) BEFORE 3170 (51.6%)1229 TOTAL Corpus: 186 TimeBank Opinion Corpus

89 89 ML Results Features – each TLINK is a feature vector: –For each event in the pair: event-class occurrence, state, reporting, i-action, i- state, aspectual, perception aspect progressive, perfective, progressive_perfecti ve modality nominal negation nominal string –tense present, past, future –signal string –shiftAspect boolean –shiftTense boolean –class { SIMULTANEOUS, IBEFORE, BEFORE, BEGINS, ENDS, INCLUDES} Link Labeling Accuracy

90 90 TLINK Extraction: Conclusion Annotated TimeML Corpus provides insufficient examples for training machine learners Significant Result: –number of examples expanded 11 times by Closure –Training learners on the expanded corpus yields excellent performance –Performance exceeds human intuitions, even when augmented with lexical rules Next steps –Integrate GUTenLink+VerbOcean rules into machine learning framework –Integrate with s2tlink and a2tlink –Feature engineering

91 91 Challenges: Temporal Reasoning Temporal reasoning for IE has used qualitative temporal relations Trivial metric relations (distances in time) can be extracted from anchored durations and sorted time expressions But commonsense metric constraints are missing –Time(Haircut) << Time(fly Boston2Sydney) First steps: –Hobbs et al. at ACL’06 –Mani & Wellner at ARTE’06 workshop

92 92 Challenges: Integrating Reasoning and Learning Reason Expand training data, globally consistent But classifier decisions may not be globally consistent Expansion substantially improves classification! Learner A < B B < C A < B B < C A < C Reason Need to integrate classification and reasoning!

93 93 Difficulties in Annotation In an interview with Barbara Walters to be shown on ABC’s “Friday nights”, Shapiro said he tried on the gloves and realized they would never fit Simpson’s larger hands. BEFORE or MEET? More coarse-grained annotation may suffice

94 94 Discourse Relations Lexical Rules from VerbOcean are still very sparse, even though they are less brittle But need to match arguments when applying lexical rules (e.g., subj/obj of push/fall) A discourse model should in fact be used

95 95 Temporal Relations as Surrogates for Rhetorical Relations When E1 is left-sibling of E2 and E1 < E2, then typically, Narration(E1, E2) When E1 is right-sibling of E2 and E1 < E2, then typically Explanation(E2, E1) When E2 is a child node of E1, then typically Elaboration(E1, E2) constraints: {Eb < Ec, Ec < Ea, Ea < Ed} a. John went into the florist shop. b. He had promised Mary some flowers. c. She said she wouldn’t forgive him if he forgot. d. So he picked out three red roses. Narr Elab Expl

96 96 TLINKS as a measure of fluency in Second Language Learning* Analyzed English oral and written proficiency samples elicited from 16 speakers of English: – 8 native speakers and 8 students in ‘Advanced’ courses in an Intensive English Program. –Corpus includes 5888 words elicited from subjects via a written narrative retelling task Chaplin’s Hard Times On average, native speakers (NSs) use significantly fewer wds to create TLinks (8.2/TLink vs for NNSs). Number of closed TLINKS for NS far exceeds the number for NNS (12,330 vs. 4924). –This means NS have, on the average, longer chains of TLINKS * Joint work with Jeff Connor-Linton at AAAL’05.

97 97 Outline Introduction Linguistic Theories AI Theories Annotation Schemes Rule-based and machine-learning methods. Challenges Links

98 98 Corpora News (newswire and broadcast) –TimeML: TimeBank, AQUAINT Corpus (all English) –TIMEX2: TIDES and TERN English Corpora, Korean Corpus (200 docs), TERN Chinese and Arabic news data (extents only) Weblogs –TIMEX2 TERN corpus (English, Chinese, Arabic – the latter with extents only) Dialogues –TIMEX2- 95 Spanish Enthusiast dialogs, and their translations Meetings –TIMEX2 Spanish portions of UN Parallel corpus (23,000 words) Children’s Stories –Reading Comprehension Exams from MITRE, Remedia: 120 stories, 20K words, CBC: 259 stories, 1/3 tagged, ~50K

99 99 Links TimeBank: (April 17, 2006) –http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LD C2006T08 TimeML: –www.timeml.org TIMEX2/TERN ACE data (English, Chinese, Arabic): –timex2.mitre.org TIMEX2/3 Tagger: –http://complingone.georgetown.edu/~linguist/GU_TIME_DOWN LOAD.HTML Korean and Spanish data.: Callisto: callisto.mitre.org

100 100 References 1.Mani, I., Pustejovsky, J., and Gaizauskas, R. (eds.). (2005) The Language of Time: A Reader. Oxford University Press. 2.Mani, I., and Schiffman, B. (2004). Temporally Anchoring and Ordering Events in News. In Pustejovsky, J. and Gaizauskas, R. (eds), Time and Event Recognition in Natural Language. John Benjamins, to appear. 3.Mani, I. (2004). Recent Developments in Temporal Information Extraction. In Nicolov, N., and Mitkov, R. Proceedings of RANLP'03, John Benjamins, to appear. 4.Jang, S., Baldwin, J., and Mani, I. (2004). Automatic TIMEX2 Tagging of Korean News. In Mani, I., Pustejovsky, J., and Sundheim, B. (eds.), ACM Transactions on Asian Language Processing: Special issue on Temporal Information Processing. 5.Mani, I., Schiffman, B., and Zhang, J. (2003). Inferring Temporal Ordering of Events in News. Short Paper. In Proceedings of the Human Language Technology Conference (HLT-NAACL'03). 6.Ferro, L., Mani, I., Sundheim, B. and Wilson G. (2001). TIDES Temporal Annotation Guidelines Draft - Version MITRE Technical Report MTR MTR 01W McLean, Virginia: The MITRE Corporation. 7.Mani, I. and Wilson, G. (2000). Robust Temporal Processing of News. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL'2000),


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