Abstract: Based on the defined knowledge base, the authors presented a Japanese time expression recognition method through combining rules set strengthened by knowledge base with statistical model. In order to increase recognition accuracy, according to the Timex2 standards’ granular classification on time, the knowledge base was progressively expanded and reconstructed given the Japanese time characteristic to achieve rules set optimization and update. Simultaneously, CRF model was fused to enhance the generalization ability of Japanese time expression recognition. The authors studied the time translation accuracy of phrase-based translation model and proved the necessity of combing rules with statistical machine translation (SMT). Experiment results show that the F1 value of Japanese time expression recognition reaches 0.8987 on open test, and both the precision and recall by the method based on rules and parallel dictionary of Japanese to Chinese time expression are a bit higher than those by the method based on statistical translation model.

Key words: knowledge base, rule, statistical model, statistical machine translation, time parallel dictionary

摘要： 基于自定义知识库, 提出一种知识库强化规则集以及与统计模型相结合的日语时间表达式识别方法,旨在不断提高时间表达式的识别精准度。按照Timex2标准对时间表现的细化分类, 结合日语时间词的特点, 渐进地扩展重构日语时间表达式知识库, 实现基于知识库获取的规则集的优化更新。同时, 融合条件随机场CRF统计模型, 提高日语时间表达式识别的泛化能力。通过考察基于短语的翻译模型翻译时间词的精度, 提出统计机器翻译(SMT)结合规则翻译日语时间词的必要性。实验结果显示, 日语时间表达式识别的开放测试F1值达到0.8987, 基于《日汉时间词平行字典》与规则的翻译精度和召回率都略高于基于统计机器翻译模型。

关键词: 知识库, 规则, 统计模型, 统计机器翻译, 时间词平行字典