C4.5: programs for machine learning
C4.5: programs for machine learning
A corpus-based approach to language learning
A corpus-based approach to language learning
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic semantic tagging of unknown proper names
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Finite-state phrase parsing by rule sequences
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
GATE: a General Architecture for Text Engineering
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
CRL/NMSU: description of the CRL/NMSU systems used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
Implementation of Croatian NERC system
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
A greek named-entity recognizer that uses support vector machines and active learning
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.