Graph-based Semantic Relatedness for Named Entity Disambiguation

Natural Language is a mean to express and discuss about concepts, objects, events, i.e. it carries semantic contents. The SemanticWeb aims at tightly coupling contents with their precise meanings. One of the ultimate roles of Natural Language Processing techniques is identifying the meaning of the text, providing effective ways to make a proper linkage between textual references and real world objects. This work adresses the problem of giving a sense to proper names in a text, that is automatically associating words representing Named Entities with their identities. The proposed methodology for Named Entity Disambiguation is based on Semantic Relatedness Scores obtained with a graph based model overWikipedia.We show that, without building a Bag of Words representation of text, but only considering named entities within the text, the proposed paradigm achieves results competitive with the state of the art on a news story dataset