Improving Word Representations via Global Context and Multiple Word Prototypes(2012ACL)
【轉載】 原文鏈接：http://blog.csdn.net/zhaoxinfan/article/details/26476419 這篇論文提出了一種獲取詞向量的方法，該方法不僅考慮詞的局部段落(local context)信息，也考慮了全局文本(global context)信息，這樣得到的詞向量不僅能表示語義信息，也能表示句法信息。同時，本文還提出了一種識別一詞多義的方法，在聚類時同一個
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相關文章
- 1. 論文筆記- Improving Word Representations via Global Context and Multiple Word Prototypes
- 2. Word Vectors、GloVe、 and Word Senses
- 3. [cs224n] Lecture 2 – Word Vectors and Word Senses
- 4. Contextual Word Representations and Pretraining
- 5. CS224n-03 Advanced Word Vector Representations
- 6. lecture02 : Word Vectors 2 and Word Senses
- 7. Deep contextualized word representations
- 8. CS224n | (2) Word Vectors and Word Senses
- 9. ELMo：Deep contextualized word representations
- 10. Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder