资源分类
》 资源 》论文》Learning Word Representations with Hierarchical Sparse Coding

Learning Word Representations with Hierarchical Sparse Coding

2020-03-04 | |  55 |   48 |   0

Abstract

We propose a new method for learning word representations using hierarchical regularization in sparse coding inspired by the linguistic study of word meanings. We show an efficient learning algorithm based on stochastic proximal methods that is significantly faster than previous approaches, making it possible to perform hierarchical sparse coding on a corpus of billions of word tokens. Experiments on various benchmark tasks—word similarity ranking, syntactic and semantic analogies, sentence completion, and sentiment analysis—demonstrate that the method outperforms or is competitive with state-of-the-art methods.

上一篇：Cascading Bandits: Learning to Rank in the Cascade Model

下一篇：Attribute Efficient Linear Regression with Distribution-Dependent Sampling

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to learn...

    The move from hand-designed features to learned...