TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text

  • Authors:

  • Naushad UzZaman;James F. Allen

  • Affiliations:

  • University of Rochester, Rochester, NY;University of Rochester, Rochester, NY

  • Venue:
  • SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems use a combination of deep semantic parsing, Markov Logic Networks and Conditional Random Field classifiers. Our two submitted systems, TRIPS and TRIOS, approached all tasks and outperformed all teams in two tasks. Furthermore, TRIOS mostly had second-best performances in other tasks. TRIOS also outperformed the other teams that attempted all the tasks. Our system is notable in that for tasks C -- F, they operated on raw text while all other systems used tagged events and temporal expressions in the corpus as input.