Named entity recognition with character-level models
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
Extracting personal names from email: applying named entity recognition to informal text
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Web-scale named entity recognition
Proceedings of the 17th ACM conference on Information and knowledge management
Name entity recognition using inductive logic programming
Proceedings of the 2010 Symposium on Information and Communication Technology
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
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Named entity recognition (NER) is a subtask in information extraction which aims to locate atomic element into predefined categories. Various NER techniques and tools have been developed to fit the interest of the applications developed. However, most NER works carried out focus on non-fiction domain. Fiction based domain displays a complex context in locating its NE especially name of person that might range from living things to non-living things. This paper proposes VAHA, automated dominant characters identification in fiction domain, particularly in fairy tales. TreeTagger, Stanford Dependencies and WordNet are the three freely available tools being used to identify verbs that are associated with human activity. The experimental results show that it is viable to use verb in identifying named entity, particularly in people category and it can be applied in a small text size environment.