基于双语LDA的跨语言文本相似度计算方法研究

A cross-lingual document similarity calculation method based on bilingual LDA

基于双语主题模型思想分析双语文本相似性,提出基于双语LDA跨语言文本相似度计算方法。先利用双语平行语料集训练双语LDA模型,再利用该模型预测新语料集主题分布,将新语料集的双语文档映射到同一个主题向量空间,结合主题分布使用余弦相似度方法计算新语料集双语文档的相似度,使用从类别间和类别内的主题分布离散度的角度改进的主题频率-逆文档频率方法计算特征主题权重。实验表明,改进后的权重计算对于基于双语LDA相似度算法的召回率有较大提高,算法对类别不受限且有较好的可靠性。

Based on the idea of bilingual topic model, we analyze similarity of bilingual documents and propose a cross-lingual document similarity calculation method based on bilingual LDA. Firstly we use the bilingual parallel documents to train the bilingual LDA model and then use the trained model to predict the topic distribution of the new corpus. The new corpus＇s bilingual documents are mapped to the vector space of the same topic. We use the cosine similarity method and topic distribution combined to calculate the similarity o{ the bilingual documents of the new corpus. We improve the topic frequency in- verse document frequency method from the aspect of the dispersion of in-category and the between-cate gory topic distribution, and utilize the improved method to calculate feature topic weights. Experimental results show that the improved weight calculation method can enhance the recall rate, enable the LDA similarity calculation algorithm not limited to certain categories, and it is reliable.