Semantic Tag Extraction from WordNet Glosses

Alina Andreevskaia, Sabine Bergler


Abstract
We propose a method that uses information from WordNet glosses to assign semantic tags to individual word meanings, rather than to entire words. The produced lists of annotated words will be used in sentiment annotation of texts and phrases and in other NLP tasks. The method was implemented in the Semantic Tag Extraction Program (STEP) and evaluated on the category of sentiment (positive, negative or neutral) using two human-annotated lists. The lists were first compared to each other and then used to assess the accuracy of the proposed system. We argue that significant disagreement on sentiment tags between the two human-annotated lists reflects a naturally occurring ambiguity of words located on the periphery of the category of sentiment. The category of sentiment, thus, is believed to be structured as a fuzzy set. Finally, we evaluate the generalizability of STEP to other semantic categories on the example of the category of words denoting increase/decrease in magnitude, intensity or quality of some state or process. The implications of this study for both semantic tagging system development and for performance evaluation practices are discussed.
Anthology ID:
L06-1341
Volume:
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
Month:
May
Year:
2006
Address:
Genoa, Italy
Editors:
Nicoletta Calzolari, Khalid Choukri, Aldo Gangemi, Bente Maegaard, Joseph Mariani, Jan Odijk, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2006/pdf/566_pdf.pdf
DOI:
Bibkey:
Cite (ACL):
Alina Andreevskaia and Sabine Bergler. 2006. Semantic Tag Extraction from WordNet Glosses. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
Cite (Informal):
Semantic Tag Extraction from WordNet Glosses (Andreevskaia & Bergler, LREC 2006)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2006/pdf/566_pdf.pdf