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Tense and Implicit Role Reference

Tense and Implicit Role Reference. Joel Tetreault University of Rochester Department of Computer Science. Implicit Role Reference. Verb phrases have certain required roles – NP’s that are expected For example: “take”: Something to take (theme) A place to take it from (from-loc)

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Tense and Implicit Role Reference

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  1. Tense and Implicit Role Reference Joel Tetreault University of Rochester Department of Computer Science

  2. Implicit Role Reference • Verb phrases have certain required roles – NP’s that are expected • For example: “take”: • Something to take (theme) • A place to take it from (from-loc) • A place to take it to (to-loc) • Something to do the taking (agent) • Possibly a tool to do the taking (instrument) • Very little work has been has been done (Poesio, 1994; Asher and Lascarides, 1998)

  3. Goal • Resolving IRR’s important to NLP • To investigate how implicit roles work • Develop an algorithm for resolving them • Evaluation of algorithm for empirical results • Use temporal information and discourse relations to improve results

  4. Outline • Implicit Roles • Annotation • Algorithm • Results • Discussion

  5. Example (1) Take engine E1 from Avon to Dansville (2a) Pick up the boxcar and take it to Broxburn [from ?] (2b) And then take the boxcar from Corning [to ?] (3a) Leave E1 there but move the boxcar down the road to Evansville. [from ?] (3b) Leave the boxcar there.

  6. Statistics: Role Distributions

  7. Corpus • Annotated a subset of the TRAINS-93 Corpus (Heeman and Allen, 1994) • 86 utterance task-oriented dialog between two humans • Task: move commodities around in a virtual world

  8. Annotation • Used sgml style annotation scheme • NP’s annotated with ID and its class (engine, tanker, location, food) • VP’s annotated with ID, event time, and roles • Roles for each verb are taken from TRIPS lexicon (Allen et al., 2000)

  9. Temporal Annotation • An event time was assigned each utterance, such as: t0, t1, u1, etc. • And constraints upon the event time are imposed: • t9>t1 (t9 comes after t1) • t9<t1 (t9 precedes t1) • t9>t1 & t9<t10 (t9 comes after t1 and before t10)

  10. Sample Annotation • U1: Take Engine E1 from Avon to Dansville. • U2: Pick up the boxcar • <ve id=ve122 time=t0 theme=ne12 from-loc=ne5 to-loc=ne6>Take <ne id=ne12>engine E1</ne> from <ne id=ne5>Avon</ne> to <ne id=ne6>Dansville</ne></ve>. • <ve id=ve123 time=t1>t0 theme=ne13 from-loc=ne6i>Pick up<ne id=ne13>the boxcar</ne></ve>.

  11. Statistics • Most implicit roles have antecedents found locally (0-2 sentences back over 90% of the time) • Instrument: 79% Instr, 10% theme, 10% ID • Theme: 88% Theme, 12% %ID • From-Loc: 62% From-Loc, 38% To-Loc • To-Loc: 57% To-Loc, 29% From-Loc, 14% Theme

  12. Algorithm • For each utterance u, process u left to right: • If NP is encountered, push it on appropriate focus stack • If VP is encountered: • place all explicit roles on top of appropriate focus stack • If role is implicit….

  13. Algorithm Example U1: Take engine E1 from Avon to Dansville Engine E1 Avon Dansville [empty] Theme From-Loc To-Loc Instrument U2: Also take the boxcar boxcar Avon Dansville [empty] Theme From-Loc To-Loc Instrument

  14. Implicit Role Algorithm • Type determines method. If role is: • Instrument: search through current utterance first for an entity that meets verb’s constraints, else go back through past utterance’s instrument and theme focus lists • Theme: same as above except search theme before instrument for each past utterance • From/To-Loc: use temporal reasoning to determine what order to search past To-Loc and From-Loc lists for each utterance:

  15. Temporal Algorithm • For two utterances uk and uj,with k > j, determine rel(uk, uj): • If time(uk) > time(uj) then rel(uk, uj) = narrative • Else rel(uk, uj) = parallel

  16. Experiment • Developed LISP system that automates the algorithm • For each marked implicit role, system tries to find an antecedent • Notations: • R-L – each focus list searched right to left (order of recency) • L-R – search is left-to-right (sentence order) • Time – algorithm augmented with temporal algorithm

  17. Results

  18. Discussion • From-Loc’s – naive version is better • To-Loc – any strategy better than naïve • Top pronoun resolution algorithms perform around 70-80% accuracy • Problems: • Corpus size – hard to make concrete conclusions or find trends • Annotation scheme is basic • Need to handle ‘return verbs’ properly • Augment system to identify whether implicit roles should be resolved or not (ignore general cases)

  19. Current Work • Building a larger corpus that can be annotated automatically using the TRIPS parser • Domain is much more varied and has different types of verbs

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