A maximum entropy approach to natural language processing
Computational Linguistics
Learning translations of named-entity phrases from parallel corpora
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Translating named entities using monolingual and bilingual resources
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning formulation and transformation rules for multilingual named entities
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
ACM Transactions on Asian Language Information Processing (TALIP)
Analysis and repair of name tagger errors
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Joint bilingual name tagging for parallel corpora
Proceedings of the 21st ACM international conference on Information and knowledge management
A joint model to identify and align bilingual named entities
Computational Linguistics
Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction
ACM Transactions on Asian Language Information Processing (TALIP)
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We observe that (1) how a given named entity (NE) is translated (i.e., either semantically or phonetically) depends greatly on its associated entity type, and (2) entities within an aligned pair should share the same type. Also, (3) those initially detected NEs are anchors, whose information should be used to give certainty scores when selecting candidates. From this basis, an integrated model is thus proposed in this paper to jointly identify and align bilingual named entities between Chinese and English. It adopts a new mapping type ratio feature (which is the proportion of NE internal tokens that are semantically translated), enforces an entity type consistency constraint, and utilizes additional monolingual candidate certainty factors (based on those NE anchors). The experiments show that this novel approach has substantially raised the type-sensitive F-score of identified NE-pairs from 68.4% to 81.7% (42.1% F-score imperfection reduction) in our Chinese-English NE alignment task.