Abstract
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.
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Goh, HN., Soon, LK., Haw, SC. (2012). VAHA: Verbs Associate with Human Activity – A Study on Fairy Tales. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_33
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DOI: https://doi.org/10.1007/978-3-642-31087-4_33
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