A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia

Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria, Sam Chapman


Abstract
Determining semantic relatedness between words or concepts is a fundamental process to many Natural Language Processing applications. Approaches for this task typically make use of knowledge resources such as WordNet and Wikipedia. However, these approaches only make use of limited number of features extracted from these resources, without investigating the usefulness of combining various different features and their importance in the task of semantic relatedness. In this paper, we propose a random walk model based approach to measuring semantic relatedness between words or concepts, which seamlessly integrates various features extracted from Wikipedia to compute semantic relatedness. We empirically study the usefulness of these features in the task, and prove that by combining multiple features that are weighed according to their importance, our system obtains competitive results, and outperforms other systems on some datasets.
Anthology ID:
L10-1203
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
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Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/292_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Ziqi Zhang, Anna Lisa Gentile, Lei Xia, José Iria, and Sam Chapman. 2010. A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
A Random Graph Walk based Approach to Computing Semantic Relatedness Using Knowledge from Wikipedia (Zhang et al., LREC 2010)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/292_Paper.pdf