Combining flat and structured approaches for temporal slot filling or: How much to compress?

Qi Li, Javier Artiles, Taylor Cassidy, Heng Ji

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

Abstract

In this paper, we present a hybrid approach to Temporal Slot Filling (TSF) task. Our method decomposes the task into two steps: temporal classification and temporal aggregation. As in many other NLP tasks, a key challenge lies in capturing relations between text elements separated by a long context. We have observed that features derived from a structured text representation can help compressing the context and reducing ambiguity. On the other hand, surface lexical features are more robust and work better in some cases. Experiments on the KBP2011 temporal training data set show that both surface and structured approaches outperform a baseline bag-of-word based classifier and the proposed hybrid method can further improve the performance significantly. Our system achieved the top performance in KBP2011 evaluation.

Original languageEnglish (US)
Title of host publicationComputational Linguistics and Intelligent Text Processing - 13th International Conference, CICLing 2012, Proceedings
Pages194-205
Number of pages12
EditionPART 2
DOIs
StatePublished - Mar 21 2012
Externally publishedYes
Event13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012 - New Delhi, India
Duration: Mar 11 2012Mar 17 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012
Country/TerritoryIndia
CityNew Delhi
Period3/11/123/17/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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