A systematic comparison of various statistical alignment models
Computational Linguistics
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Inducing information extraction systems for new languages via cross-language projection
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
pre-CODIE: crosslingual on-demand information extraction
NAACL-Demonstrations '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Demonstrations - Volume 4
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Who, what, when, where, why?: comparing multiple approaches to the cross-lingual 5W task
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Cross-lingual slot filling from comparable corpora
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
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In this paper, we discuss the performance of cross-lingual information extraction systems employing an automatic pattern acquisition module. This module, which creates extraction patterns starting from a user's narrative task description, allows rapid customization to new extraction tasks. We compare two approaches: (1) acquiring patterns in the source language, performing source language extraction, and then translating the resulting templates to the target language, and (2) translating the texts and performing pattern discovery and extraction in the target language. We demonstrate an average of 8--10% more recall using the first approach. We discuss some of the problems with machine translation and their effect on pattern discovery which lead to this difference in performance.