An entity linking method for microblog based on semantic categorization by word embeddings

As a widely applied task in natural language processing (NLP), named entity linking (NEL) is to link a given mention to an unambiguous entity in knowledge base. NEL plays an important role in information extraction and question answering. Since contents of microblog are short, traditional algorithms for long texts linking do not fit the microblog linking task well. Precious studies mostly constructed models based on mentions and its context to disambiguate entities, which are difficult to identify candidates with similar lexical and syntactic features. In this paper, we propose a novel NEL method based on semantic categorization through abstracting in terms of word embeddings, which can make full use of semantic involved in mentions and candidates. Initially, we get the word embeddings through neural network and cluster the entities as features. Then, the candidates are disambiguated through predicting the categories of entities by multiple classifiers. Lastly, we test the method on dataset of NLPCC2014, and draw the conclusion that the proposed method gets a better result than the best known work, especially on accurancy.