Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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
Who Is It? Context Sensitive Named Entity and Instance Recognition by Means of Wikipedia
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Finding lists of people on the web
ACM SIGCAS Computers and Society
A subcategorization acquisition system for French verbs
HLT-SRWS '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Student Research Workshop
Where's the verb?: correcting machine translation during question answering
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Context and Domain Knowledge Enhanced Entity Spotting in Informal Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
The role of named entities in web people search
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Simple coreference resolution with rich syntactic and semantic features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Using narrative functions as a heuristic for relevance in story understanding
Proceedings of the Intelligent Narrative Technologies III Workshop
Name entity recognition using inductive logic programming
Proceedings of the 2010 Symposium on Information and Communication Technology
Extracting social networks from literary fiction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Chinese named entity recognition based on hierarchical hybrid model
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
VerbNet class assignment as a WSD task
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Automatic identification of protagonist in fairy tales using verb
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
An approach for named entity recognition in poorly structured data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Polarity preference of verbs: what could verbs reveal about the polarity of their objects?
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
LaTeCH '12 Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Extracting and modeling durations for habits and events from Twitter
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
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Named entity recognition (NER) is a subtask in information extraction which aims to locate atomic element into predefined types. 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, specifically whereby its characters could be represented in diverse spectrums, ranging from living things (animals, plants, and person) to non-living things (vehicle, furniture). Motivated by a hypothesis such that there always exists verb specifically describes human being conduct, in this paper, we propose a NER system which aims to identify NEs that perform human activity based on verb analysis (VAHA) in an autonomous manner. More specifically, our approach attempts to identify dominant character (DC) by studying the nature of verb that associates with human activity via TreeTagger, Stanford packages and WordNet. Our experimental results validate our initial hypothesis that NEs can be accurately identified by referring to the associated verbs that associate with human activity. Our empirical study also proves that the approach is applicable to small text size articles. Another significant contribution of our approach is that it does not require training data set and anaphora resolution.