Cross-lingual news article comparison using bi-graph clustering and Siamese-LSTM
JSAI2017（2017年度 人工知能学会全国大会（第31回）), 2017/5
機械学習 (Machine Learning) データサイエンス (Data Science) その他の取り組み (Misc.)
- 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 a model by feeding news text that is closely related in the financial domain to help us to analyze the relationship among news reports written in different languages.
Cross-lingual news article comparison using bi-graph clustering and Siamese-LSTM（外部サイト／External Site Link）