An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
The Application of Semantic Classification Trees to Natural Language Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coping with ambiguity and unknown words through probabilistic models
Computational Linguistics - Special issue on using large corpora: II
Disambiguation of proper names in text
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
The multilingual named entity recognition framework
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Unsupervised named entity classification models and their ensembles
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Relevance measures for question answering, the LIA at QA@CLEF-2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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This paper describes a supervised learning method to automatically select from a set of noun phrases, embedding proper names of different semantic classes, their most distinctive features. The result of the learning process is a decision tree which classifies an unknown proper name on the basis of its context of occurrence. This classifier is used to estimate the probability distribution of an out of vocabulary proper name over a tagset. This probability distribution is itself used to estimate the parameters of a stochastic part of speech tagger.