Unsupervised approaches to metonymy recognition

Yves Peirsman


Abstract
To this day, the automatic recognition of metonymies has generally been addressed with supervised approaches. However, these require the annotation of a large number of training instances and hence, hinder the development of a wide-scale metonymy recognition system. This paper investigates if this knowledge acquisition bottleneck in metonymy recognition can be resolved by the application of unsupervised learning. Although the investigated technique, Schütze’s (1998) algorithm, enjoys considerable popularity in Word Sense Disambiguation, I will show that it is not yet robust enough to tackle the specific case of metonymy recognition. In particular, I will study the influence on its performance of four variables—the type of data set, the size of the context window, the application of SVD and the type of feature selection.
Anthology ID:
2006.jeptalnrecital-recital.7
Volume:
Actes de la 13ème conférence sur le Traitement Automatique des Langues Naturelles. REncontres jeunes Chercheurs en Informatique pour le Traitement Automatique des Langues
Month:
April
Year:
2006
Address:
Leuven, Belgique
Editors:
Piet Mertens, Cédrick Fairon, Anne Dister, Patrick Watrin
Venue:
JEP/TALN/RECITAL
SIG:
Publisher:
ATALA
Note:
Pages:
709–718
Language:
URL:
https://aclanthology.org/2006.jeptalnrecital-recital.7
DOI:
Bibkey:
Cite (ACL):
Yves Peirsman. 2006. Unsupervised approaches to metonymy recognition. In Actes de la 13ème conférence sur le Traitement Automatique des Langues Naturelles. REncontres jeunes Chercheurs en Informatique pour le Traitement Automatique des Langues, pages 709–718, Leuven, Belgique. ATALA.
Cite (Informal):
Unsupervised approaches to metonymy recognition (Peirsman, JEP/TALN/RECITAL 2006)
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PDF:
https://aclanthology.org/2006.jeptalnrecital-recital.7.pdf