NAMED ENTITY DISAMBIGUATION: A HYBRID APPROACH

DOI

10.1080/18756891.2012.747661How to use a DOI?

Keywords

Entity disambiguation, Entity linking, Named entity, Knowledge base, Wikipedia

Abstract

Semantic annotation of named entities for enriching unstructured content is a critical step in development of Semantic Web and many Natural Language Processing applications. To this end, this paper addresses the named entity disambiguation problem that aims at detecting entity mentions in a text and then linking them to entries in a knowledge base. In this paper, we propose a hybrid method, combining heuristics and statistics, for named entity disambiguation. The novelty is that the disambiguation process is incremental and includes several rounds that filter the candidate referents, by exploiting previously identified entities and extending the text by those entity attributes every time they are successfully resolved in a round. Experiments are conducted to evaluate and show the advantages of the proposed method. The experiment results show that our approach achieves high accuracy and can be used to construct a robust entity disambiguation system.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hien T. Nguyen
AU  - Tru H. Cao
PY  - 2012
DA  - 2012/11/01
TI  - NAMED ENTITY DISAMBIGUATION: A HYBRID APPROACH
JO  - International Journal of Computational Intelligence Systems
SP  - 1052
EP  - 1067
VL  - 5
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.747661
DO  - 10.1080/18756891.2012.747661
ID  - Nguyen2012
ER  -