基于语义知识的词义消歧

2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT) Pub Date : 2019-01-01 DOI:10.1109/ICEICT.2019.8846408

Rui-Yan Liang, Chun-Yi Luo, Chun-Xiang Zhang, Tian-Yi Lei, Hua Wang, Ming-Zhe Li

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引用次数: 0

摘要

词义消歧是机器翻译和信息检索领域的一个重要研究热点。本文提出了一种新的WSD方法。对含有歧义词的汉语句子进行分词。在查阅《通义词林》后，将其左右词语义范畴作为消歧特征。基于判别特征，采用贝叶斯模型对歧义词进行正确的语义分类。训练数据集用于优化贝叶斯模型，测试数据集用于测试WSD分类器的性能。实验表明，WSD分类器的准确率得到了提高。

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Word Sense Disambiguation Based on Semantic Knowledge

Word sense disambiguation (WSD) is an important and hot research topic in machine translation and information retrieval. A new WSD method is proposed in this paper. Chinese sentence containing ambiguous word is segmented into words. Its left and right words’ semantic categories are used as disambiguation features after Tongyici Cilin is consulted. Based on discriminative features, bayesian model is applied to select correct semantic categories for ambiguous words. Training data set is used to optimize bayesian model and test data set is utilized to test the performance of WSD classifier. Experiments show that accuracy of WSD classifier is improved.

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2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT)

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