Abstract
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.
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Ye, Y., Zhang, Z. (2005). Tense Tagging for Verbs in Cross-Lingual Context: A Case Study. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_77
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DOI: https://doi.org/10.1007/11562214_77
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