Morph Resolution Based on Autoencoders Combined with Effective Context Information

In social networks, people often create morphs, a special type of fake alternative names for avoiding internet censorship or some other purposes. How to resolve these morphs to the entities that they really refer to is very important for natural language processing tasks. Although some methods have been proposed, they do not use the context information of morphs or target entities effectively; only use the information of neighbor words of morphs or target entities. In this paper, we proposed a new approach to resolving morphs based on autoencoders combined with effective context information. First, in order to represent the semantic meanings of morphs or target candidates more precisely, we proposed a method to extract effective context information. Next, by integrating morphs or target candidates and their effective context information into autoencoders, we got the embedding representation of morphs and target candidates. Finally, we ranked target candidates based on similarity measurement of semantic meanings of morphs and target candidates. Thus, our method needs little annotated data, and experimental results demonstrated that our approach can significantly outperform state-of-the-art methods.