Citation

Goh, Hui Ngo and Soon, Lay Ki and Haw, Su Cheng (2013) Automatic dominant character identification in fables based on verb analysis – Empirical study on the impact of anaphora resolution. Knowledge-Based Systems, 54. pp. 147-162. ISSN 0950-7051

Text Automatic dominant character identification in fables based on verb analysis – Empirical study on the impact of anaphora resolution.pdf Restricted to Repository staff only Download (1MB)

Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	17 Feb 2014 09:26
Last Modified:	17 Jul 2014 08:21
URII:	http://shdl.mmu.edu.my/id/eprint/5252

Downloads

Edit (login required)