Cover not available
Part of
Linguistic Approaches to Emotions in Context
Edited by Fabienne H. Baider and Georgeta Cislaru
[Pragmatics & Beyond New Series 241] 2014
► pp. 99–112

A corpus-based construction 
of emotion verb scales

| Department of Computer Science, Princeton University, USA
| CNRS LLF – Université Paris 7, France

We examine subclasses of English verbs of emotion, in particular Experiencer verbs like surprise, fear and astonish, which express the causation of gradable emotions. The gradation is richly lexicalized by verbs that denote different degrees of intensity of the same emotion (e.g., surprise, strike, dumbfound, flabbergast). We examine manually constructed groups of verbs expressing different degrees of intensity of one underlying emotion in the light of corpus (Web) data. We employ lexical-semantic patterns that were previously identified as discriminating between the members of pairs of scalar adjectives and apply them to verbs that belong to three broad emotion verbs classes. The results show, first, that the chosen verbs indeed possess scalar qualities; second, they confirm the prior assignment of the verbs into broad classes based on a common underlying emotion; finally, the Web data allow us to construct consistent scales with verbs ordered according to the intensity of the emotion. Future extensions to additional verb classes will lead to a more subtle analysis of the semantics of emotion verbs and their improved representation in lexical resources with potential benefits for translation, language pedagogy and automatic inferencing.

Published online: 13 March 2014
DOI logo
https://doi.org/10.1075/pbns.241.07fel
References
Anscombre, Jean-Claude, and Oswald Ducrot
1983 L’argumentation dans la Langue. Philosophie et Langage. Brussels: Pierre Mardaga.Google Scholar
Belletti, Adriana, and Luigi Rizzi
1988 “Psych-verbs and θ–theory.” Natural Language and Linguistic Theory 6: 291–352. DOI logoGoogle Scholar
Bierwisch, Manfred, and Ewald Lang
(ed) 1989 Dimensional Adjectives: Grammatical Structure and Conceptual Interpretation, Berlin: Springer. DOI logoGoogle Scholar
Boyd-Graber, Jordan, Christiane Fellbaum, Daniel Osherson, and Robert Schapire
2006 “Adding dense, weighted connections to WordNet.” Third Global WordNet Meeting. Jeju Island, Korea.Google Scholar
Chklovski, Timothy, and Patrick Pantel
2004 “VERBOCEAN: Mining the Web for fine-Grained semantic verb relations.” In Proceedings of EMNLP2004: 33–40.Google Scholar
Cruse, D. Alan.
1986 Lexical Semantics. Cambridge: Cambridge University Press.Google Scholar
Esuli, Andrea, and Fabrizio Sebastiani
2006 “SentiWordNet: A publicly available resource for opinion mining.” In Proceedings of LREC, Genoa.Google Scholar
Fellbaum, Christiane
1990 “The English verb lexicon as a semantic Net.” International Journal of Lexicography 3 (4): 278–301. DOI logoGoogle Scholar
1995 “Co-occurrence and antonymy.” International Journal of Lexicography 8 (4): 281–303. DOI logoGoogle Scholar
(ed) 1998 WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.Google Scholar
Hatzivassiloglou, Vasileios, and Kathleen R. McKeown.
1997 “Predicting the semantic orientation of adjectives.” In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics: 175–181.Google Scholar
Hearst, Marti
1992 “Automatic acquisition of hyponyms from large text corpora.” In Proceedings of COLING1992: 539–545.Google Scholar
Horn, Laurence R.
1989 A Natural History of Negation. Chicago: University of Chicago Press. Google Scholar
Johnson-Laird, N. Philip, and Keith J. Oatley
1989 “The language of emotions: An analysis of a semantic field.” Cognition and Emotion 3:81–123. DOI logoGoogle Scholar
Le Grand Robert de la langue française
2007.Google Scholar
Kennedy, Christopher
1999 Projecting the Adjective. New York & London: Garland Publishing.Google Scholar
Levin, Beth
1993 English Verb Classes and Alternations: A Preliminary Investigation. Chicago: University of Chicago Press.Google Scholar
Martin, James R., and Peter R.R. White.
2005 The Language of Evaluation. Palgrave Macmillan. DOI logoGoogle Scholar
Mass, Andrew L., Andrew Y. Ng, and Christopher Pott
2011 Multi-Dimensional Sentiment Analysis with Learned Representations. Stanford University: Technical report.Google Scholar
Mathieu, Y.
Yannick 2005 “A computational semantic lexicon of French verbs of emotion.” In Computing Attitude and Affect in Text, ed. by G. Shanahan, Y. Qu, and J. Wiebe, 109–123. Dordrecht: Springer.Google Scholar
Mathieu, Y. Yannick, and Christiane Fellbaum
2010 “Verbs of emotion in French and English.” In Proceedings of The 5th Global WordNet Conference, 70–75.Google Scholar
Mel’čuk, Igor, and Wanner, Leo.
1996 “Lexical functions and lexical inheritance for emotion lexemes in German.” In Lexical Functions in Lexicography and Natural Language Processing, ed. by L. Wanner, 209–277. Amsterdam/Philadelphia: Benjamins.Google Scholar
Miller, George A.
1995 “Wordnet: A lexical database for English.” Communications of the ACM 38: 39–41. DOI logoGoogle Scholar
Miller, Katherine J.
1998 “Modifiers in WordNet.” In WordNet. An Electronic Lexical Database, ed. by Christiane Fellbaum, 47–67. Cambridge, MA: MIT Press.Google Scholar
Ortony, Andrew, Gerald L. Clore, and Allan Collins.
1988 The Cognitive Structure of Emotions, Cambridge: Cambridge University press. DOI logoGoogle Scholar
Schulam, Peter, and Christiane Fellbaum
2010 “Automatically determining the semantic gradation of German adjectives.” In Proceedings of The 10th Conference on Natural Language Processing (KONVENS 2010), ed. By Manfred Pinkal, Ines Rehbein, Sabine Schulte im Walde, and Angelika Storrer, 163–168. Saarbruecken: Saarland University Press. [URL]Google Scholar
Sheinman, Vera, and Tokunaga, Takenobu.
2009 “AdjScale: Visualizing differences between adjectives for language learners.” IEICE Transaction of Information and Systems Vol. E92-D (8): 1542–1550. DOI logoGoogle Scholar
Sheinman, Vera, Christiane Fellbaum, Isaac Julien, Peter Schulam, and Takenobu Tokunaga
In press “Large, huge, or gigantic: Identifying and encoding intensity relations among adjectives in WordNet.” Lexical Resources and Evaluation.
Snow, Rion, Daniel Jurafsky, and Andrew Y. Ng
2006 “Semantic taxonomy induction from heterogenous evidence.” In Proceedings of COLING/ACL2006:801–808.Google Scholar
Trésor de la langue française informatisé
[URL]
Turney, Peter
2002 “Thumps up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, Pennsylvania.Google Scholar
Yu, Hong, and Vasileios Hatzivassiloglou
2003 “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences.” In Proceedings of EMNLP2003: 129–136.Google Scholar
Wilson, Theresa, Janyce Wiebe, and Rebecca Hwa
2004 “Just how mad are you? Finding strong and weak opinion clauses.” In Proceedings of AAAI-04, 21st Conference of the American Association for Artificial Intelligence: 761–769.Google Scholar
Wierzbicka, Anna
1996 Semantics. Primes and Universals. Oxford: Oxford University Press.Google Scholar
Cited by

Cited by 1 other publications

Huminski, Aliaksandr, Fiona Liausvia & Arushi Goel
2023. Semantic Roles in VerbNet and FrameNet: Statistical Analysis and Evaluation. In Computational Linguistics and Intelligent Text Processing [Lecture Notes in Computer Science, 13452], ► pp. 135 ff. DOI logo

This list is based on CrossRef data as of 20 april 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.