Host: The Japanese Society for Artificial Intelligence
Name : The 31st Annual Conference of the Japanese Society for Artificial Intelligence, 2017
Number : 31
Location : [in Japanese]
Date : May 23, 2017 - May 26, 2017
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.