MUSE: Modularizing unsupervised sense embeddings
Journal
EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
Pages
327-337
Date Issued
2017
Author(s)
Lee G.-H
Abstract
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. ? 2017 Association for Computational Linguistics.
Subjects
Benchmarking; Reinforcement learning; Benchmark data; Coarse-grained; Contextual words; Exploration techniques; Flexible modules; Modular approach; Selection mechanism; State-of-the-art performance; Natural language processing systems
Type
conference paper