Timelines from text: Identification of syntactic temporal relations

Steven Bethard, James H. Martin, Sara Klingenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations

Abstract

We propose and evaluate a linguistically motivated approach to extracting temporal structure necessary to build a timeline. We considered pairs of events in a verb-clause construction, where the first event is a verb and the second event is the head of a clausal argument to that verb. We selected all pairs of events in the TimeBank that participated in verb-clause constructions and annotated them with the labels BEFORE, OVERLAP and AFTER. The resulting corpus of 895 event-event temporal relations was then used to train a machine learning model. Using a combination of event-level features like tense and aspect with syntax-level features like the paths through the syntactic tree, we were able to train a support vector machine (SVM) model which could identify new temporal relations with 89.2% accuracy. High accuracy models like these are a first step towards automatic extraction of timeline structures from text.

Original languageEnglish (US)
Title of host publicationICSC 2007 International Conference on Semantic Computing
Pages11-18
Number of pages8
DOIs
StatePublished - 2007
Externally publishedYes
EventICSC 2007 International Conference on Semantic Computing - Irvine CA, United States
Duration: Sep 17 2007Sep 19 2007

Publication series

NameICSC 2007 International Conference on Semantic Computing

Conference

ConferenceICSC 2007 International Conference on Semantic Computing
Country/TerritoryUnited States
CityIrvine CA
Period9/17/079/19/07

ASJC Scopus subject areas

  • General Computer Science
  • Computer Science Applications

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