A maximum entropy approach to natural language processing
Computational Linguistics
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
Exploiting domain structure for named entity recognition
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Leveraging context in user-centric entity detection systems
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
ANERsys: An Arabic Named Entity Recognition System Based on Maximum Entropy
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Arabic named entity recognition using optimized feature sets
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A deep learning approach to machine transliteration
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
CRF-based active learning for Chinese named entity recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Fusion of multiple features for chinese named entity recognition based on CRF model
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Maximum entropy translation model in dependency-based MT framework
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Assessing the challenge of fine-grained named entity recognition and classification
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Domain adaptation of rule-based annotators for named-entity recognition tasks
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
SVM based learning system for information extraction
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Recognition of chinese personal names based on CRFs and law of names
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Image retrieval and annotation using maximum entropy
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Joint inference of entities, relations, and coreference
Proceedings of the 2013 workshop on Automated knowledge base construction
Aggregating semantic annotators
Proceedings of the VLDB Endowment
Effective named entity recognition for idiosyncratic web collections
Proceedings of the 23rd international conference on World wide web
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In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional non-annotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.