Using @Twitter Conventions to Improve #LOD-Based Named Entity Disambiguation

Genevieve Gorrell, Johann Petrak, Kalina Bontcheva

Abstract: State-of-the-art named entity disambiguation approaches tend to perform poorly on social media content, and microblogs in particular. Tweets are processed individually and the richer, microblog-specific context is largely ignored. This paper focuses specifically on quantifying the impact on entity disambiguation performance when readily available contextual information is included from URL content, hash tag definitions, and Twitter user profiles. In particular, including URL content significantly improves performance. Similarly, user profile information for @mentions improves recall by over 10 % with no adverse impact on precision. We also share a new corpus of tweets, which have been hand-annotated with DBpedia URIs, with high inter-annotator agreement.