The rapidly increasing use of large-scale data on the Web makes named entity disambiguation become one of the main challenges to research in Information Extraction and development of Semantic Web. This paper presents a novel method for detecting proper names in a text and linking them to the right entities in Wikipedia. The method is hybrid, containing two phases of which the first one utilizes some heuristics and patterns to narrow down the candidates, and the second one employs the vector space model to rank the ambiguous cases to choose the right candidate. The novelty is that the disambiguation process is incremental and includes several rounds that filter the candidates, by exploiting previously identified entities and extending the text by those entity attributes every time they are successfully resolved in a round. We test the performance of the proposed method in disambiguation of names of people, locations and organizations in texts of the news domain. The experiment results show that our approach achieves high accuracy and can be used to construct a robust named entity disambiguation system.
['Hien T. Nguyen', 'Tru H. Cao']
Named Entity Disambiguation: A Hybrid Statistical and Rule-Based Incremental Approach