Learning Word Sense Embeddings from Word Sense Definitions
arXiv:1606.04835
Abstract
Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.
To appear at NLPCC-ICCPOL 2016
References in corpus (5)
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- ADADELTA: An Adaptive Learning Rate Method
- Reasoning about Entailment with Neural Attention
- An Ensemble Method to Produce High-Quality Word Embeddings (2016)
- Joint Word Representation Learning using a Corpus and a Semantic Lexicon