Foundations of statistical natural language processing
Foundations of statistical natural language processing
Cross-Language Information Retrieval
Cross-Language Information Retrieval
Handbook of Natural Language Processing
Handbook of Natural Language Processing
Computational Linguistics - Special issue on web as corpus
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Identifying word translations in non-parallel texts
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Mixed language query disambiguation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Word sense acquisition from bilingual comparable corpora
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Processing comparable corpora with Bilingual Suffix Trees
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Sentence alignment for monolingual comparable corpora
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Extracting parallel sub-sentential fragments from non-parallel corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Cross Sentence Alignment for Structurally Dissimilar Corpus Based on Singular Value Decomposition
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Improved statistical machine translation using monolingually-derived paraphrases
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Extracting parallel sentences from comparable corpora using document level alignment
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Two ways to use a noisy parallel news corpus for improving statistical machine translation
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
Paraphrase fragment extraction from monolingual comparable corpora
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Generalizing sub-sentential paraphrase acquisition across original signal type of text pairs
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We propose a completely unsupervised method for mining parallel sentences from quasi-comparable bilingual texts which have very different sizes, and which include both in-topic and off-topic documents. We discuss and analyze different bilingual corpora with various levels of comparability. We propose that while better document matching leads to better parallel sentence extraction, better sentence matching also leads to better document matching. Based on this, we use multi-level bootstrapping to improve the alignments between documents, sentences, and bilingual word pairs, iteratively. Our method is the first method that does not rely on any supervised training data, such as a sentence-aligned corpus, or temporal information, such as the publishing date of a news article. It is validated by experimental results that show a 23% improvement over a method without multilevel bootstrapping.