A Simple Approach to Learn Polysemous Word Embeddings
This addresses the need for efficient polysemous word representation in NLP, offering a simpler alternative to more complex existing methods.
The paper tackles the problem of learning polysemous word embeddings by proposing a simple method that uses single-sense representations and coefficients learned in one pass, achieving excellent results compared to state-of-the-art models in unsupervised learning.
Many NLP applications require disambiguating polysemous words. Existing methods that learn polysemous word vector representations involve first detecting various senses and optimizing the sense-specific embeddings separately, which are invariably more involved than single sense learning methods such as word2vec. Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings. In this paper, we propose a simple method to learn a word representation, given any context. Our method only requires learning the usual single sense representation, and coefficients that can be learnt via a single pass over the data. We propose several new test sets for evaluating word sense induction, relevance detection, and contextual word similarity, significantly supplementing the currently available tests. Results on these and other tests show that while our method is embarrassingly simple, it achieves excellent results when compared to the state of the art models for unsupervised polysemous word representation learning.