TimeML compliant text analysis for Temporal Reasoning Branimir Boguraev and Rie Kubota Ando

Introduction Events in documents can be partially described with temporal expressions Reasoning about events requires a more sophisticated representation TimeML provides a rich format for temporal annotation Annotating documents in TimeML is hard Only small reference corpora are available

Introduction ACE 2004 includes a task for capturing atomic pieces of time information from text Applications require advanced temporal reasoning, possibly over multiple documents –Document summarisation –Temporal ordering of events in news –Question answering

Introduction Boguraev and Ando describe a framework for temporal IE The process uses TimeML for event representation Goals are to develop a useful and reusable framework for reasoning about events

TimeML SGML-like annotation Aims to fully capture all time related information in a document, not just temporal expressions Uses TIMEX3 format for temporal expressions EVENT, SIGNAL and LINK tags note events and temporal relations

TimeBank Major TimeML corpus Small documents, 68.5K words 1400 temporal expressions 8200 events

Task Find TIMEX3s Assign canonical time references Mark and type EVENTs Associate EVENTs with TIMEX3s where possible

Method A set of temporal points is constructed form TimeML annotated data This set is then translated into a graph of intervals, points and temporal relations A separate component maps this graph to an ontological representation of time FOL is separated from text analysis

Method TIMEX3 expressions are found using a set of FSGs Essentially, a parse tree is built for processing data into TIMEX3 format An additional discourse-level discovery step is performed to hand ambiguous and underspecified expressions

Method FSGs are interleaved with NER This helps detect events and links that are semantically present but not obvious All optional fields of each TIMEX3 found are populated Discourse time reference is used as anchor for canonical times

Results Lenient EVENT recognition in WSJ is % accurate Strict EVENT matching (including EVENT type) drops to 61-64% Strict figure Lower than average NER performance EVENT typing task is difficult

Results Only TLINKS that pair EVENT and TIMEX3 are considered TLINKed token proximity threshold is varied in order to adjust task complexity Trying to identify TLINKS within 4 tokens provides the strongest results F-measure below 60%

Results Adding FS grammar information to feature set provides small performance boost Increasing EVENT/TIMEX3 search distance to 64 tokens has performance of 22% FS grammar information in this case brings performance over 50%

Analysis System is capable of spotting relations Correctly typing relations is difficult DURING and IS_INCLUDED are particularly hard to distinguish

Conclusion TimeBank’s small size is a hindrance The lack of diversity of tags makes training hard Most ML approaches prefer larger datasets The system shows that it’s possible to extract data from TimeML discourse and correctly identify temporal information