跨语言文本主题发现是跨语言文本挖掘领域的重要研究方向,对跨语言文本分析和组织各种文本数据具有较高的应用价值.基于Bagging和跨语言词嵌入改进LDA主题模型,提出跨语言文本主题发现方法BCL-LDA(Bagging,Cross-lingual word embedding with LDA),从多语言文本中挖掘关键信息.该方法首先将Bagging集成学习思想与LDA主题模型结合生成混合语言子主题集;然后利用跨语言词嵌入和K-means算法对混合子主题进行聚类分组;最后使用TF-IDF算法对主题词进行过滤排序.汉语-德语、汉语-法语主题发现实验表明,该方法在主题连贯性和多样性方面均表现优异,能够提取出语义更加相关且主题更加连贯多样的双语主题.
Cross-lingual Text Topic Discovery Based on Ensemble Learning
Cross-lingual text topic discovery is an important research direction in the field of cross-lingual text mining,and it has high application value for cross-lingual text analysis and organization of various text data.Based on Bagging and cross-lingual word embedding to improve the LDA topic model,a cross-lingual text topic discovery method BCL-LDA(Bagging,cross-lingual word embedding with LDA)is proposed to mine key information from multilingual text.This method first combines the Bagging integrated learning idea with the LDA topic model to generate a mixed language subtopic set.Then it uses cross-lingual word em-bedding and K-means algorithm to cluster and group the mixed subtopics.Finally,the TF-IDF algorithm is used to filter and sort the subject words.The Chinese-German and Chinese-French topic discovery experiments show that this method performs well in terms of topic coherence and diversity,and can extract bilingual topics with more relevant semantics and more coherent and di-verse topics.
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