【摘要】 【目的】建立未登录词识别模型,提升发现自然科学领域文本中未登录词的能力,同时降低人工干预成本。【方法】在假设的基础上,构建条件随机场(CRFs)与领域本体元素集相结合的未登录词识别模型。以生物多样性文本为样本,通过比较不同模型性能的差异,检验假设,验证模型的合理性。【结果】实验结果表明,CRFs模型选择单纯的字、字词混合序列、字词混合序列及默认词性、字词混合序列及含自定义语义功能标记的词性为特征时,未登录词识别能力依次提升。该结果证明研究假设为真,本文建立的模型科学、合理。【局限】模型标注未登录词的准确性有待提升。【结论】该模型具有更强的未登录词识别能力,同时可以极大地降低人工建立训练集的成本。更多还原

【Abstract】 [Objective] Establish a model to improve the out-of-vocabulary identification capability, reduce the cost of manual intervention. [Methods] On the basis of the hypothesis, a out-of-vocabulary identification model is set up combining CRFs and domain Ontology elements set. Using biodiversity text as samples, the rationality of the model is verified by comparing the performance differences among models and testing hypothesis. [Results] The experimental results show that the model established by this study has the best identification capability. The results prove that the hypothesis is true, and the model is reasonable and scientific. [Limitations] The tagging accuracy of the model remains to be improved. [Conclusions] The model established in this paper has better identification capability, while greatly reducing the cost of artificial training dataset.更多还原