The Journal of Machine Learning Research
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity measures for tracking information flow
Proceedings of the 14th ACM international conference on Information and knowledge management
A translation model for sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Power shifts in web-based translation memory
Machine Translation
Exploration of term dependence in sentence retrieval
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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There are large numbers of well-translated sentence pairs on the Web, which can be used for translating sentences in different languages. It is an interesting problem to search the closest sentence translations from the Web for high-quality translation, which has attracted significant attention recently. However, it is not straightforward to develop an effective approach, as this task heavily depends on the effectiveness of the similarity model which is used to quantify the similarity between two sentences. In this paper, we propose several optimization techniques to address this problem. We devise a phrase-based model to quantify the similarity between two sentences. We judiciously select high-quality phrases from sentences, which can capture the key features of sentences and thus can be used to quantify similarity between sentences. Experimental results show that our approach has performance advantages compared with the state-of-the-art sentence matching methods