The Tanl tagger for named entity recognition on transcribed broadcast news at Evalita 2011
Berardi G., Attardi G., Dei Rossi S., Simi M.
Named Entity Recognition Maximum Entropy Dynamic programming
The Tanl tagger is a configurable tagger based on a Maximum Entropy classifier, which uses dynamic programming to select the best sequences of tags. We applied it to the NER tagging task, customizing the set of features to use, and including features deriving from dictionaries extracted from the training corpus. The final accuracy of the tagger is further improved by applying simple heuristic rules.
Source: Evaluation of Natural Language and Speech Tools for Italian. International Workshop. Revised selected papers, edited by Bernardo Magnini, Francesco Cutugno, Mauro Falcone, Emanuele Pianta, pp. 116–125. Berlin: Springer, 2013
Publisher: Springer, Berlin, DEU
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@inbook{oai:it.cnr:prodotti:277230,
title = {The Tanl tagger for named entity recognition on transcribed broadcast news at Evalita 2011},
author = {Berardi G. and Attardi G. and Dei Rossi S. and Simi M.},
publisher = {Springer, Berlin, DEU},
doi = {10.1007/978-3-642-35828-9_13},
booktitle = {Evaluation of Natural Language and Speech Tools for Italian. International Workshop. Revised selected papers, edited by Bernardo Magnini, Francesco Cutugno, Mauro Falcone, Emanuele Pianta, pp. 116–125. Berlin: Springer, 2013},
year = {2013}
}