Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding
Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li · 2017
Integrating text and knowledge into a unified semantic space has attracted significant research interests recently.However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities.In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base.In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense.In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings.Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.