Cross-language article linking with deep neural network based paragraph encoding. (March 2022) - Wang, Yu-Chun; Chuang, Chia-Min; Wu, Chun-Kai; Pan, Chao-Lin; Tsai, Richard Tzong-Han
Abstract: Cross-language article linking (CLAL), the task of generating links between articles in different languages from different encyclopedias, is critical for facilitating sharing among online knowledge bases. Some previous CLAL research has been done on creating links among Wikipedia wikis, but much of this work depends heavily on simple language patterns and encyclopedia format or metadata. In this paper, we propose a new CLAL method based on deep learning paragraph embeddings to link English Wikipedia articles with articles in Baidu Baike, the most popular online encyclopedia in mainland China. To measure article similarity for link prediction, we employ several neural networks with attention mechanisms, such as CNN and LSTM, to train paragraph encoders that create vector representations of the articles' semantics based only on article text, rather than link structure, as input data. Using our "Deep CLAL" method, we compile a data set consisting of Baidu Baike entries and corresponding English Wikipedia entries. Our approach does not rely on linguistic or structural features and can be easily applied to other language pairs by using pre-trained word embeddings, regardless of whether the two languages are on the same encyclopedia platform. Highlights: Cross-language article linking helps create a multilingual unified knowledge base. Using attention-based neural network that learns to attend to the vital part of articles. The novel method that does not rely on feature engineering and is scalable to large data.