An experimental study of a hybrid entity recognition and linking system

We present an experimental study of the performance of a hybrid semantic and linguistic system for recognizing and linking entities from formal and informal texts. In the current literature, systems are generally tailored to one or a few types of textual documents (e.g. narrative texts, newswire articles, informal text such as microposts). In contrast, we assess the performance of a hybrid approach that adapts the entity extraction, recognition and linking process to the type of document being analyzed. The hybrid system relies on POS taggers, gazetteers and Twitter user account dereferencing modules to extract entities,

NER modules to recognize and type entities and entity popularity, string distance measures and scoring functions to disambiguate entities by ranking potential link candidates. The evaluation results show the robustness of our proposed approach in terms of text-independence compared to the current state-of-the art.