Dissecting word embeddings and language models in natural language processing-Reference-Cited by-同舟云学术

Dissecting word embeddings and language models in natural language processing

Published:2021-07-04 Issue:5 Volume:24 Page:1509-1515
ISSN:0972-0529
Container-title:Journal of Discrete Mathematical Sciences and Cryptography
language:en
Short-container-title:Journal of Discrete Mathematical Sciences and Cryptography

Author:

Verma Vivek Kumar¹,Pandey Mrigank¹,Jain Tarun²,Tiwari Pradeep Kumar³

Affiliation:

1. Department of Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, India

2. Department of Computer Science & Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India

3. Department of Computer Applications, Manipal University Jaipur, Jaipur 303007, Rajasthan, India

Publisher

Informa UK Limited

Subject

Applied Mathematics,Algebra and Number Theory,Analysis

Link

https://www.tandfonline.com/doi/pdf/10.1080/09720529.2021.1968108

Reference16 articles.

1. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. “Efficient estimation of word representations in vector space.” CoRR, abs/1301.3781, 2013.

2. Yoav Goldberg and Omer Levy. “word2vec Explained: Deriving Mikolov, et al.’s Negative-Sampling Word-Embedding Method.” arXiv:1402.3722 [cs.CL], 2014.

3. A Survey on Techniques in NLP

4. From static to dynamic word representations: a survey

5. Bamler, Robert and Stephan Mandt. “Dynamic Word Embeddings.” ICML (2017).

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