Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Combining data-driven systems for improving Named Entity Recognition
Data & Knowledge Engineering
Czech named entity corpus and SVM-based recognizer
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Named entities in Czech: annotating data and developing NE tagger
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
SWSNL: Semantic Web Search Using Natural Language
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Named Entity Recognition (NER) is an important preprocessing tool for many Natural Language Processing tasks like Information Retrieval, Question Answering or Machine Translation. This paper is focused on NER for Czech language. The proposed NER is based on knowledge and experiences acquired on other languages and adapted for Czech. Our recognizer outperforms the previously introduced recognizers for Czech. The article is also focused on the use of semantic spaces for NER. Although no significant improvement was yet achieved in this way, we believe that the research is worth of sharing.