Addressing the MFS Bias in WSD systems

Marten Postma, Ruben Izquierdo, Eneko Agirre, German Rigau, Piek Vossen

Abstract
Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses. We addressed the MFS bias in WSD systems by combining the output from a WSD system with a set of mostly static features to create a MFS classifier to decide when to and not to choose the MFS. The output from this MFS classifier, which is based on the Random Forest algorithm, is then used to modify the output from the original WSD system. We applied our classifier to one of the state-of-the-art supervised WSD systems, i.e. IMS, and to of the best state-of-the-art unsupervised WSD systems, i.e. UKB. Our main finding is that we are able to improve the system output in terms of choosing between the MFS and the less frequent senses. When we apply the MFS classifier to fine-grained WSD, we observe an improvement on the less frequent sense cases, whereas we maintain the overall recall.
Anthology ID:
L16-1268
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1695–1700
Language:
URL:
https://aclanthology.org/L16-1268
DOI:
Bibkey:
Cite (ACL):
Marten Postma, Ruben Izquierdo, Eneko Agirre, German Rigau, and Piek Vossen. 2016. Addressing the MFS Bias in WSD systems. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1695–1700, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Addressing the MFS Bias in WSD systems (Postma et al., LREC 2016)
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https://preview.aclanthology.org/nschneid-patch-2/L16-1268.pdf
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