TY - GEN
T1 - A hybrid approach to NER by MEMM and manual rules
AU - Fresko, Moshe
AU - Rosenfeld, Binyamin
AU - Feldman, Ronen
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
KW - Information Extraction
KW - Machine Learning
KW - Maximum Entropy Markov Model
KW - Named Entity Recognition
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=33745795816&partnerID=8YFLogxK
U2 - 10.1145/1099554.1099667
DO - 10.1145/1099554.1099667
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AN - SCOPUS:33745795816
SN - 1595931406
SN - 9781595931405
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 361
EP - 362
BT - CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
T2 - CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
Y2 - 31 October 2005 through 5 November 2005
ER -