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CIKM
2005
Springer

A hybrid approach to NER by MEMM and manual rules

10 years 3 months ago
A hybrid approach to NER by MEMM and manual rules
This paper describes a framework for defining domain specific Feature Functions in a user friendly form to be used in a Maximum Entropy Markov Model (MEMM) for the Named Entity Recognition (NER) task. Our system called MERGE allows defining general Feature Function Templates, as well as Linguistic Rules incorporated into the classifier. The simple way of translating these rules into specific feature functions are shown. We show that MERGE can perform better from both purely machine learning based systems and purely-knowledge based approaches by some small expert interaction of rule-tuning. Categories and Subject Descriptors I.2.7 [Natural Language Processing]: Text analysis – Named Entity Recognition, Information Extraction. General Terms Named Entity, Information, Document Collection, Statistical Model, Empirical Model, Machine Learning, Maximum Entropy. Keywords Named Entity Recognition, Text Mining, Machine Learning, Information Extraction, Maximum Entropy Markov Model.
Moshe Fresko, Binyamin Rosenfeld, Ronen Feldman
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CIKM
Authors Moshe Fresko, Binyamin Rosenfeld, Ronen Feldman
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