5,803 research outputs found
SupWSD: a flexible toolkit for supervised word sense disambiguation
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at http://github.com/SI3P/SupWSD
MUSE: Modularizing Unsupervised Sense Embeddings
This paper proposes to address the word sense ambiguity issue in an
unsupervised manner, where word sense representations are learned along a word
sense selection mechanism given contexts. Prior work focused on designing a
single model to deliver both mechanisms, and thus suffered from either
coarse-grained representation learning or inefficient sense selection. The
proposed modular approach, MUSE, implements flexible modules to optimize
distinct mechanisms, achieving the first purely sense-level representation
learning system with linear-time sense selection. We leverage reinforcement
learning to enable joint training on the proposed modules, and introduce
various exploration techniques on sense selection for better robustness. The
experiments on benchmark data show that the proposed approach achieves the
state-of-the-art performance on synonym selection as well as on contextual word
similarities in terms of MaxSimC
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