Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking

In this paper, we describe our approach for Named Entity Recognition in Twitter, a shared task for ACL 2015 Workshop on Noisy User-generated Text Because of the noisy, short, and colloquial nature of Twitter, the performance of Named Entity Recognition (NER) degrades significantly. To address this problem, we propose a novel method to enhance the performance of the Twitter NER task by using Entity Linking which is a method for detecting entity mentions in text and resolving them to corresponding entries in knowledge bases such as Wikipedia. Our method is based on supervised machine-learning and uses the highquality knowledge obtained from several open knowledge bases. In comparison with the other systems proposed for this shared task, our method achieved the best performance.