Abstract
ABSTRACTThis 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.
Highlights
Recognizing entity mentions in a text and linking them to entities in a knowledge base are two fundamental tasks in text analysis
Instead of using lexical and semantic features to identify the boundary of an entity mention as Named Entity Recognition (NER), Milne and Witten (2008) introduced the Link Detection problem, in which a short-and-meaningful sequence of terms is identified as a relevant mention of an entity if there is a highly statistical relation between the term sequence and the mention
This section examines the effect of Co-reference Resolution (CR) on the performance of EREL
Summary
Recognizing entity mentions in a text and linking them to entities in a knowledge base are two fundamental tasks in text analysis. Instead of using lexical and semantic features to identify the boundary of an entity mention as NER, Milne and Witten (2008) introduced the Link Detection problem, in which a short-and-meaningful sequence of terms is identified as a relevant mention of an entity if there is a highly statistical relation between the term sequence and the mention. In this problem, the identification of mentions is driven by the knowledge base, in which an entity can be a named entity or a nominal entity. Compared to using NER to identify entities, Link Detection can recognize named entities, common nouns and other entities as adjectives or gerunds
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