Cross-lingual News Article Comparison Using Bi-graph Clustering and Siamese-LSTM

抄録

Calculating similarity score for monolingual text is a popular task since it could be used for various text mining system. However seldom research is focusing on multilingual text resources. On the other hand, machine learning based algorithms such as CBOW word embedding and clustering are widely used in extracting features of text. In this research, we develop and train a model that could calculate the similarity of the two finance news reports, by utilizing CBOW, spherical clustering, bi-graph extraction as well as the Siamese-LSTM deep learning model. In the end, we train the model by feeding news data that is closely related in the financial domain to help us to analyze the relationship among news reports written in different languages.