arXiv (Cornell University)· 2013· Preprint
Efficient Estimation of Word Representations in Vector Space
- 18,154citations
- 2013year
Short summary
This paper introduces two new model architectures that significantly improve the accuracy and reduce the computational cost of creating word vector representations from massive datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced two novel model architectures for word vector representation.
- Achieved large improvements in accuracy compared to previous techniques.
- Reduced computational cost significantly, learning from a 1.6 billion word dataset in under a day.
- Demonstrated state-of-the-art performance on syntactic and semantic similarity tasks.
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 previously 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 performance on our test set for measuring syntactic and semantic word similarities.
The authors' abstract, as published at the source. arXiv (Cornell University), 2013 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Artificial Intelligence
Artificial IntelligenceComputer Science