Named Entity Based Document Similarity with SVM-Based Re-ranking for Entity Linking View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Ayman Alhelbawy , Rob Gaizauskas

ABSTRACT

In this paper we present a novel approach to search a knowledge base for an entry that contains information about a named entity (NE) mention as specified within a given context. A document similarity function (NEBSim) based on NE co-occurrence has been developed to calculate the similarity between two documents given a specific NE mention in one of them. NEBsim is also used in conjunction with the traditional cosine similarity measure to learn a model for ranking. Naive Bayes and SVM classifiers are used to re-rank the retrieved documents. Our experiments, carried out on TAC-KBP 2011 data, show NEBsim achieves significant improvement in accuracy as compared with a cosine similarity approach. They also show that re-ranking using learn to rank techniques can significantly improve the accuracy at high ranks. More... »

PAGES

379-388

Book

TITLE

Advanced Machine Learning Technologies and Applications

ISBN

978-3-642-35325-3
978-3-642-35326-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-35326-0_38

DOI

http://dx.doi.org/10.1007/978-3-642-35326-0_38

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1031192468


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