EREL: an Entity Recognition and Linking algorithm

• Main Authors: Cuong Duc Nguyen, Trong Hai Duong
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Journal of Information and Telecommunication
• Subjects: Entity Recognition; Entity Linking; Co-reference Resolution; information retrieval; text analysis; Disambiguation
• Online Access: http://dx.doi.org/10.1080/24751839.2017.1372073

This paper introduces the EREL algorithm that integrates Entity Recognition, Co-reference Resolution (CR) and Disambiguation. The algorithm recognizes entity mentions as the longest name based on the name dictionary constructed from the Wikipedia data. The CR is integrated into the algorithm to improve the performance in processing short-form or abbreviated names. The algorithm employs a new approach in disambiguation entities using new features as entity-level context information and case-sensitive data about the mention in disambiguation. Tested on four benchmark data sets in the GERBIL framework, EREL outperforms the current Entity Linking methods. EREL achieves the micro f-score as 0.83 in both tasks Disambiguate to Wikipedia and Annotate to Wikipedia.