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RESLVE: leveraging user interest to improve entity disambiguation on short text

We address the Named Entity Disambiguation (NED) problem for short, user-generated texts on the social Web. In such settings, the lack of linguistic features and sparse lexical context result in a high degree of ambiguity and sharp performance drops of nearly 50% in the accuracy of conventional NED systems. We handle these challenges by developing a general model of user-interest with respect to a personal knowledge context and instantiate it using Wikipedia. We conduct systematic evaluations using individuals' posts from Twitter, YouTube, and Flickr and demonstrate that our novel technique is able to achieve performance gains beyond state-of-the-art NED methods.