Cross-lingual slot filling from comparable corpora

  • Authors:
  • Matthew Snover;Xiang Li;Wen-Pin Lin;Zheng Chen;Suzanne Tamang;Mingmin Ge;Adam Lee;Qi Li;Hao Li;Sam Anzaroot;Heng Ji

  • Affiliations:
  • City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY;City University of New York, New York, NY

  • Venue:
  • BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
  • Year:
  • 2011

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Abstract

This paper introduces a new task of crosslingual slot filling which aims to discover attributes for entity queries from crosslingual comparable corpora and then present answers in a desired language. It is a very challenging task which suffers from both information extraction and machine translation errors. In this paper we analyze the types of errors produced by five different baseline approaches, and present a novel supervised rescoring based validation approach to incorporate global evidence from very large bilingual comparable corpora. Without using any additional labeled data this new approach obtained 38.5% relative improvement in Precision and 86.7% relative improvement in Recall over several state-of-the-art approaches. The ultimate system outperformed monolingual slot filling pipelines built on much larger monolingual corpora.