Named Entity Extraction and Linking Challenge: University of Twente at #Microposts2014

Twitter is a potentially rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing (NLP) tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Linking (NEL) for tweets. Although NEE and NEL are two topics that are well studied in literature, almost all approaches treated the two problems separately. We believe that disambiguation (linking) could help improving the extraction process. We call this potential for mutual improvement, the reinforcement effect. It mimics the way humans understand natural language. Furthermore, our proposed approaches handles uncertainties involved in the two processes by considering possible alternatives.