A maximum entropy approach to natural language processing
Computational Linguistics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Computational Linguistics - Special issue on using large corpora: II
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Refined lexicon models for statistical machine translation using a maximum entropy approach
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Semantic maps for word alignment in bilingual parallel corpora
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Word to word alignment strategies
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Improving word alignment quality using morpho-syntactic information
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A maximum entropy approach to combining word alignments
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Context-dependent alignment models for statistical machine translation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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Typically, statistical alignment models are based on single-word dependencies. These models do not include contextual information, which can lead to inadequate alignments. In this paper, we present an approach to include contextual dependencies in the statistical alignment model by using a refined lexicon model. Unlike previous work, we directly integrate this in the EM algorithm of statistical alignment models. Experimental results are given for the French-English Canadian Parliament Hansards task and the Verbmobil task. The evaluation is performed by comparing the obtained alignments with a manually annotated reference alignment.