Using Query Patterns to Learn the Durations of Events

1. Using Query Patterns to Learn the Durations of Events AndreyGusev joint work withNate Chambers, PranavKhaitan, DivyeKhilnani, Steven Bethard, Dan Jurafsky

2. Examples of Event Durations • Talk to a friend – minutes • Driving – hours • Study for an exam – days • Travel – weeks • Run a campaign – months • Build a museum – years

3. Why are we interested in durations? • Event Understanding • Duration is an important aspectual property • Can help build timelines and events • Event coreference • Duration may be a cue that events are coreferent • Gender (learned from the web) helps nominal coreference • Integration into search products • Query: “healthy sleep time for age groups” • Query: “president term length in [country x]”

4. How can we learn event durations? Approach1: Supervised System

5. Dataset (Pan et al., 2006) Labeled 58 documents from TimeBank with event durations Average of minimum and maximum labeled durations A Brooklyn woman who was watching her clothes dry in a laundromat. Min duration – 5 min Max Duration – 1 hour Average – 1950 seconds

6. Original Features (Pan et al., 2006) Event Properties Event token, lemma, POS tag Subject and Object Head word of syntactic subject and objects of the event, along with their lemmas and POS tags. Hypernyms WordNethypernyms for the event, its subject and its object. Starting from the first synset of each lemma, three hyperhyms were extracted from the WordNet hierarchy.

7. New Features Event Attributes Tense, aspect, modality, event class Named Entity Class of Subjects and Objects Person, organization, locations, or other. Typed Dependencies Binary feature for each typed dependency Reporting Verbs Binary feature for reporting verbs (say, report, reply, etc.)

8. Limitations of the Supervised Approach Need explicitly annotated datasets Sparse and limited data Limited to the annotated domain Low inter-annotator agreement More than a Day and Less Than a Day– 87.7% Duration Buckets – 44.4% Approximate Duration Buckets– 79.8%

9. Overcoming Supervised Limitations Statistical Web Count approach Lots of text/data that can be used Not limited to the annotated domain Implicit annotations from many sources Hearst(1998), Ji and Lin (2009)

10. How can we learn event durations? Approach 2: Statistical Web Counts

11. Terms - Durations Buckets and Distributions “talked for * seconds” “talked for * minutes” “talked for * hours” “talked for * days” “talked for * weeks” “talked for * months” “talked for * years” - 1638 hits - 61816 hits - 68370 hits - 4361 hits - 3754 hits - 5157 hits - 103336 hits Duration Bucket Distribution

12. Two Duration Prediction Tasks Coarse grained prediction “Less than a day” or “Longer than a day” Fine grained prediction Second, minute, hour, etc.

13. Task 1: Coarse Grained Prediction

14. Yesterday Pattern for Coarse Grained Task <eventpast> yesterday <eventpastp> yesterday eventpast = past tense eventpastp= past progressive tense Normalize yesterday event pattern counts with counts of event occurrence in general Average the two ratios Find threshold on the training set

15. Example: “to say” with Yesterday Pattern “said yesterday” – 14,390,865 hits “said” – 1,693,080,248 hits “was saying yesterday” – 29,626 hits “was saying” – 14,167,103 hits Average Ratio = 0.0053

16. Threshold for Yesterday Pattern

17. Task 2: Fine Grained Prediction

18. Fine Grained Durations from Web Counts Said • How long does the event “X” last? • Ask the web: • “X for * seconds” • “X for * minutes” • … • Output distribution over time units

19. Not All Time Units are Equal • Need to look at the base distribution • “for * seconds” • “for * minutes” • … • In habituals, etc. people like to say “for years”

20. Conditional Frequencies for Buckets Said • Divide • “X for * seconds” • By • “for * seconds” • Reduce credit for seeing “X for years”

21. Double Peak Distribution • Two interpretations • Durative • Iterative • Distributions show that with two peaks

22. Merging Patterns Said • Multiple patterns • Distributions averaged • Reduces noise from individual patterns • Pattern needs to have greater than 100 and less 100,000 hits

23. Fine Grained Patterns • Used Patterns • <eventpast> for * <bucket> • <eventpastp> for * <bucket> • spent * <bucket> <eventger> • Patterns not used • <eventpast> in * <bucket> • takes * <bucket> to <event> • <eventpast> last <bucket>

24. Evaluation and Results

25. Evaluation • TimeBank annotations (Pan, Mulkar and Hobbs 2006) • Coarse Task: Greater or less than a day • Fine Task: Time units (seconds, minutes, hours, …, years) • Counted as correct if within 1 time unit • Baseline: Majority Class • Fine Grained – months • Coarse Grained – greater than a day • Compare with re-implementation of supervised (Pan, Mulkar and Hobbs 2006)

26. New Split for TimeBank Dataset Train – 1664 events (714 unique verbs) Test – 471 events (274 unique verbs) TestWSJ – 147 events (84 unique verbs) Split info is available at http://cs.stanford.edu/~agusev/durations/

27. Web Counts System Scoring Fine grained Smooth over the adjacent buckets and select top bucket score(bi) = bi-1 + bi + bi+1 Coarse grained “Yesterday” classifier with a threshold (t = 0.002) Use fine grained approach Select coarse grained bucket based on fine grained bucket

28. Results Web counts perform as well as the fully supervised system

29. Backoff Statistics (“Spent” Pattern) Events in training dataset Had at least 10 hits

30. Effect of the Event Context • Supervised classifier use context in their features • Web counts system doesn’t use context of the events • Significantly fewer hits when including context • Better accuracy with more hits than with context • What is the effect of subject/object context on the understanding of event duration?

31. Can humans do this task without context? Human Annotation:Mechanical Turk

32. MTurk Setup • 10 MTurk workers for each event • Without the context • Event – choice for each duration bucket • With the context • Event with subject/object – choice for each duration bucket

33. Sometimes Context Doesn’t Matter Exploded Intolerant

34. Web counts vs. Turk distributions “said” (web count) “said” (MTurk)

35. Web counts vs. Turk distributions “looking” (web count) “looking” (MTurk)

36. Web counts vs. Turk distributions “considering” (web count) “considering” (MTurk)

37. Results: Mechanical Turk Annotations • Compare accuracy • Event with context • Event without context Context significantly improves accuracy of MTurk annotations

38. Event Duration Lexicon • Distributions for 1000 most frequent verbs from the NYT portion of the Gigaword with 10 most frequent grammatical objects of each verb • Due to thresholds not all the events have distributions • EVENT=to use, • ID=e13-7, • OBJ=computer, • PATTERNS=2, • DISTR=[0.009;0.337;0.238;0.090;0.130;0.103;0.092;0.002;] • http://cs.stanford.edu/~agusev/durations/

39. Summary • We learned aspectual information from the web • Event durations from the web counts are as accurate as a supervised system • Web counts are domain-general, work well even without context • New lexicon with 1000 most frequent verbs with 10 most frequent objects • MTurk suggests that context can improve accuracy of event duration annotation

40. Thanks! Questions?