Corpus-Based Computation of Reverse Associations

Reinhard Rapp


Abstract
According to psychological learning theory an important principle governing language acquisition is co-occurrence. For example, when we perceive language, our brain seems to unconsciously analyze and store the co-occurrence patterns of the words. And during language production, these co-occurrence patterns are reproduced. The applicability of this principle is particularly obvious in the case of word associations. There is evidence that the associative responses people typically come up with upon presentation of a stimulus word are often words which frequently co-occur with it. It is thus possible to predict a response by looking at co-occurrence data. The work presented here is along these lines. However, it differs from most previous work in that it investigates the direction from the response to the stimulus rather than vice-versa, and that it also deals with the case when several responses are known. Our results indicate that it is possible to predict a stimulus word from its responses, and that it helps if several responses are given.
Anthology ID:
L14-1216
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1380–1386
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/221_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Reinhard Rapp. 2014. Corpus-Based Computation of Reverse Associations. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1380–1386, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Corpus-Based Computation of Reverse Associations (Rapp, LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/221_Paper.pdf