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

Implicit Role Reference n n Verb phrases have certain required roles – NP’s that Implicit Role Reference n n Verb phrases have certain required roles – NP’s that are expected For example: “take”: n n n 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 done (Poesio, 1994; Asher and Lascarides, 1998)

Goal n n n Resolving IRR’s important to NLP To investigate how implicit roles Goal n n n 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

Outline n n n Implicit Roles Annotation Algorithm Results Discussion Outline n n n Implicit Roles Annotation Algorithm Results Discussion

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

Statistics: Role Distributions Role Total Explicit Implicit Instrument 25 6 19 Theme 40 30 Statistics: Role Distributions Role Total Explicit Implicit Instrument 25 6 19 Theme 40 30 10 From-Loc 39 13 26 To-Loc 27 20 7 Total 131 69 62

Corpus n n n Annotated a subset of the TRAINS-93 Corpus (Heeman and Allen, Corpus n n n 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

Annotation n n Used sgml style annotation scheme NP’s annotated with ID and its Annotation n n 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)

Temporal Annotation n n An event time was assigned each utterance, such as: t Temporal Annotation n n An event time was assigned each utterance, such as: t 0, t 1, u 1, etc. And constraints upon the event time are imposed: n n n t 9>t 1 (t 9 comes after t 1) t 9t 1 & t 9

Sample Annotation n n U 1: Take Engine E 1 from Avon to Dansville. Sample Annotation n n U 1: Take Engine E 1 from Avon to Dansville. U 2: Pick up the boxcar Take engine E 1 from Avon to Dansville. t 0 theme=ne 13 fromloc=ne 6 i>Pick upthe boxcar.

Statistics n n n Most implicit roles have antecedents found locally (0 -2 sentences Statistics n n n 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: 57% To-Loc, 29% From-Loc, 14% Theme

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

Algorithm Example U 1: Take engine E 1 from Avon to Dansville Engine E Algorithm Example U 1: Take engine E 1 from Avon to Dansville Engine E 1 Theme Avon Dansville From-Loc To-Loc [empty] Instrument U 2: Also take the boxcar Avon Dansville Theme From-Loc To-Loc [empty] Instrument

Implicit Role Algorithm n Type determines method. If role is: n n n Instrument: Implicit Role Algorithm n Type determines method. If role is: n n n 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:

Temporal Algorithm n n n For two utterances uk and uj, with k > Temporal Algorithm n n n 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

Experiment n n n Developed LISP system that automates the algorithm For each marked Experiment n n n Developed LISP system that automates the algorithm For each marked implicit role, system tries to find an antecedent Notations: n n n 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

Results Algorithm Instrument Theme From-Loc To-Loc Overall Baseline 42. 1% 11. 1% 88. 5% Results Algorithm Instrument Theme From-Loc To-Loc Overall Baseline 42. 1% 11. 1% 88. 5% 44. 5% 57. 1% R-L 78. 9% 55. 6% 65. 4% 22. 2% 61. 9% L-R 78. 9% 44. 4% 88. 5% 44. 5% 73. 0% Time, L-R 78. 9% 55. 6% 61. 5% 55. 6% 65. 1% Time, R-L 78. 9% 44. 5% 69. 3% 55. 6% 66. 7% Total 19 9 26 9 63

Discussion n n From-Loc’s – naive version is better To-Loc – any strategy better Discussion n n From-Loc’s – naive version is better To-Loc – any strategy better than naïve Top pronoun resolution algorithms perform around 70 -80% accuracy Problems: n n 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)

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