Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Table extraction using conditional random fields
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Applying machine learning to Chinese temporal relation resolution
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
LAC '06 Proceedings of the Workshop on Frontiers in Linguistically Annotated Corpora 2006
Tense sense disambiguation: a new syntactic polysemy task
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
N-gram-based tense models for statistical machine translation
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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The current work applies Conditional Random Fields to the problem of temporal reference mapping from Chinese text to English text. The learning algorithm utilizes a moderate number of linguistic features that are easy and inexpensive to obtain. We train a tense classifier upon a small amount of manually labeled data. The evaluation results are promising according to standard measures as well as in comparison with a pilot tense annotation experiment involving human judges. Our study exhibits potential value for full-scale machine translation systems and other natural language processing tasks in a cross-lingual scenario.