Joint learning of contextal and global features for named entity disambiguation

Named entity disambiguation (NED) is an important stage in Natural Language Processing (NLP) which automatically resolves mentions to entities in a given knowledge base (KB) like Wikipedia. NED is a complex and challenging problem due to the inherent ambiguity between real world mentions and the entities they refer to. Most existing studies use hand-crafted features to represent mentions, context and entities, which is labor intensive. In this paper, we address this problem by presenting a new NED model which combining local, context and global evidence. By leveraging the learned mixed dense word-level and topic-level representations and the graph-based disambiguation approach, context and global features are well captured. Experiments for NED are conducted on AIDA dataset, which show that the proposed model can obtain state-of-the-art results.