arXiv (Cornell University)· 2013· Preprint
Efficient Estimation of Word Representations in Vector Space
- 11,737citations
- 2013year
Short summary
New neural network architectures achieve state-of-the-art word similarity accuracy with significantly lower computational cost, learning high-quality word vectors from a 1.6 billion word dataset in under a day.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced two novel neural network architectures for word vector representation.
- Achieved state-of-the-art accuracy on word similarity tasks.
- Significantly reduced computational cost, learning from 1.6 billion words in under a day.
- Demonstrated superior performance in measuring syntactic and semantic similarities.
AI-generated from the title and abstract; the full text is not read.
Abstract
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previ-ously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art perfor-mance on our test set for measuring syntactic and semantic word similarities.
The authors' abstract, as published at the source. arXiv (Cornell University), 2013 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science