Problems With Evaluation of Word Embeddings Using Word Similarity Tasks
Abstract
Lacking standardized extrinsic evaluation methods for vector representations of words, the NLP community has relied heavily on word similarity tasks as a proxy for intrinsic evaluation of word vectors. Word similarity evaluation, which correlates the distance between vectors and human judgments of semantic similarity is attractive, because it is computationally inexpensive and fast. In this paper we present several problems associated with the evaluation of word vectors on word similarity datasets, and summarize existing solutions. Our study suggests that the use of word similarity tasks for evaluation of word vectors is not sustainable and calls for further research on evaluation methods.
Cite
@article{arxiv.1605.02276,
title = {Problems With Evaluation of Word Embeddings Using Word Similarity Tasks},
author = {Manaal Faruqui and Yulia Tsvetkov and Pushpendre Rastogi and Chris Dyer},
journal= {arXiv preprint arXiv:1605.02276},
year = {2016}
}
Comments
The First Workshop on Evaluating Vector Space Representations for NLP