Translating named entities using monolingual and bilingual resources
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Named entity transliteration with comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Named entity transliteration and discovery from multilingual comparable corpora
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Language specific issue and feature exploration in Chinese event extraction
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Challenges from information extraction to information fusion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhancing multi-lingual information extraction via cross-media inference and fusion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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
Mining entity translations from comparable corpora: a holistic graph mapping approach
Proceedings of the 20th ACM international conference on Information and knowledge management
Parallel sentence generation from comparable corpora for improved SMT
Machine Translation
Unsupervised language-independent name translation mining from Wikipedia infoboxes
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
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This paper describes a new task to extract and align information networks from comparable corpora. As a case study we demonstrate the effectiveness of this task on automatically mining name translation pairs. Starting from a small set of seeds, we design a novel approach to acquire name translation pairs in a bootstrapping framework. The experimental results show this approach can generate highly accurate name translation pairs for persons, geopolitical and organization entities.