Word2vec (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Word2vec" in English language version.

refsWebsite

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13arxiv.org

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8doi.org

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5nih.gov

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2harvard.edu

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2semanticscholar.org

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2radimrehurek.com

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2aclweb.org

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1code.google.com

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1espacenet.com

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1ieee.org

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1worldcat.org

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1groups.google.com

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1mit.edu

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1cambridge.org

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1ssrn.com

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1github.com

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1springer.com

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aclweb.org

Levy, Omer; Goldberg, Yoav; Dagan, Ido (2015). "Improving Distributional Similarity with Lessons Learned from Word Embeddings". Transactions of the Association for Computational Linguistics. 3. Transactions of the Association for Computational Linguistics: 211–225. doi:10.1162/tacl_a_00134.
Arora, S; et al. (Summer 2016). "A Latent Variable Model Approach to PMI-based Word Embeddings". Transactions of the Association for Computational Linguistics. 4: 385–399. arXiv:1502.03520. doi:10.1162/tacl_a_00106 – via ACLWEB.

arxiv.org

Mikolov, Tomas; et al. (2013). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg S.; Dean, Jeff (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems. arXiv:1310.4546. Bibcode:2013arXiv1310.4546M.
Goldberg, Yoav; Levy, Omer (2014). "word2vec Explained: Deriving Mikolov et al.'s Negative-Sampling Word-Embedding Method". arXiv:1402.3722 [cs.CL].
Rong, Xin (5 June 2016), word2vec Parameter Learning Explained, arXiv:1411.2738
Von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen (2022). "On the validity of pre-trained transformers for natural language processing in the software engineering domain". IEEE Transactions on Software Engineering. 49 (4): 1487–1507. arXiv:2109.04738. doi:10.1109/TSE.2022.3178469. ISSN 1939-3520. S2CID 237485425.
Le, Quoc; Mikolov, Tomas (May 2014). "Distributed Representations of Sentences and Documents". Proceedings of the 31st International Conference on Machine Learning. arXiv:1405.4053.
Nay, John (21 December 2017). "Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text". SSRN. arXiv:1609.06616. SSRN 3087278.
Angelov, Dimo (August 2020). "Top2Vec: Distributed Representations of Topics". arXiv:2008.09470 [cs.CL].
Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.
Ng, Patrick (2017). "dna2vec: Consistent vector representations of variable-length k-mers". arXiv:1701.06279 [q-bio.QM].
Arora, S; et al. (Summer 2016). "A Latent Variable Model Approach to PMI-based Word Embeddings". Transactions of the Association for Computational Linguistics. 4: 385–399. arXiv:1502.03520. doi:10.1162/tacl_a_00106 – via ACLWEB.
Jansen, Stefan (9 May 2017). "Word and Phrase Translation with word2vec". arXiv:1705.03127. {{cite journal}}: Cite journal requires |journal= (help)
Altszyler, E.; Ribeiro, S.; Sigman, M.; Fernández Slezak, D. (2017). "The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text". Consciousness and Cognition. 56: 178–187. arXiv:1610.01520. doi:10.1016/j.concog.2017.09.004. PMID 28943127. S2CID 195347873.

cambridge.org

Rheault, Ludovic; Cochrane, Christopher (3 July 2019). "Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora". Political Analysis. 28 (1).

code.google.com

"Google Code Archive - Long-term storage for Google Code Project Hosting". code.google.com. Retrieved 13 June 2016.

doi.org

Mikolov, Tomáš; Karafiát, Martin; Burget, Lukáš; Černocký, Jan; Khudanpur, Sanjeev (26 September 2010). "Recurrent neural network based language model". Interspeech 2010. ISCA: ISCA. pp. 1045–1048. doi:10.21437/interspeech.2010-343.
Von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen (2022). "On the validity of pre-trained transformers for natural language processing in the software engineering domain". IEEE Transactions on Software Engineering. 49 (4): 1487–1507. arXiv:2109.04738. doi:10.1109/TSE.2022.3178469. ISSN 1939-3520. S2CID 237485425.
Campello, Ricardo; Moulavi, Davoud; Sander, Joerg (2013). "Density-Based Clustering Based on Hierarchical Density Estimates". Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science. Vol. 7819. pp. 160–172. doi:10.1007/978-3-642-37456-2_14. ISBN 978-3-642-37455-5.
Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.
Banerjee, Imon; Chen, Matthew C.; Lungren, Matthew P.; Rubin, Daniel L. (2018). "Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort". Journal of Biomedical Informatics. 77: 11–20. doi:10.1016/j.jbi.2017.11.012. PMC 5771955. PMID 29175548.
Levy, Omer; Goldberg, Yoav; Dagan, Ido (2015). "Improving Distributional Similarity with Lessons Learned from Word Embeddings". Transactions of the Association for Computational Linguistics. 3. Transactions of the Association for Computational Linguistics: 211–225. doi:10.1162/tacl_a_00134.
Arora, S; et al. (Summer 2016). "A Latent Variable Model Approach to PMI-based Word Embeddings". Transactions of the Association for Computational Linguistics. 4: 385–399. arXiv:1502.03520. doi:10.1162/tacl_a_00106 – via ACLWEB.
Altszyler, E.; Ribeiro, S.; Sigman, M.; Fernández Slezak, D. (2017). "The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text". Consciousness and Cognition. 56: 178–187. arXiv:1610.01520. doi:10.1016/j.concog.2017.09.004. PMID 28943127. S2CID 195347873.

espacenet.com

worldwide.espacenet.com

US 9037464, Mikolov, Tomas; Chen, Kai & Corrado, Gregory S. et al., "Computing numeric representations of words in a high-dimensional space", published 2015-05-19, assigned to Google Inc.

github.com

Angelov, Dimo (11 November 2022). "Top2Vec". GitHub.

groups.google.com

"Parameter (hs & negative)". Google Groups. Retrieved 13 June 2016.

harvard.edu

ui.adsabs.harvard.edu

Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg S.; Dean, Jeff (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems. arXiv:1310.4546. Bibcode:2013arXiv1310.4546M.
Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.

ieee.org

ieeexplore.ieee.org

Von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen (2022). "On the validity of pre-trained transformers for natural language processing in the software engineering domain". IEEE Transactions on Software Engineering. 49 (4): 1487–1507. arXiv:2109.04738. doi:10.1109/TSE.2022.3178469. ISSN 1939-3520. S2CID 237485425.

mit.edu

jmlr.csail.mit.edu

"Visualizing Data using t-SNE" (PDF). Journal of Machine Learning Research, 2008. Vol. 9, pg. 2595. Retrieved 18 March 2017.

nih.gov

pubmed.ncbi.nlm.nih.gov

Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.
Banerjee, Imon; Chen, Matthew C.; Lungren, Matthew P.; Rubin, Daniel L. (2018). "Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort". Journal of Biomedical Informatics. 77: 11–20. doi:10.1016/j.jbi.2017.11.012. PMC 5771955. PMID 29175548.
Altszyler, E.; Ribeiro, S.; Sigman, M.; Fernández Slezak, D. (2017). "The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text". Consciousness and Cognition. 56: 178–187. arXiv:1610.01520. doi:10.1016/j.concog.2017.09.004. PMID 28943127. S2CID 195347873.

ncbi.nlm.nih.gov

Asgari, Ehsaneddin; Mofrad, Mohammad R.K. (2015). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.
Banerjee, Imon; Chen, Matthew C.; Lungren, Matthew P.; Rubin, Daniel L. (2018). "Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort". Journal of Biomedical Informatics. 77: 11–20. doi:10.1016/j.jbi.2017.11.012. PMC 5771955. PMID 29175548.

radimrehurek.com

Rehurek, Radim. "Gensim".
"Gensim - Deep learning with word2vec". Retrieved 10 June 2016.

semanticscholar.org

api.semanticscholar.org

Von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen (2022). "On the validity of pre-trained transformers for natural language processing in the software engineering domain". IEEE Transactions on Software Engineering. 49 (4): 1487–1507. arXiv:2109.04738. doi:10.1109/TSE.2022.3178469. ISSN 1939-3520. S2CID 237485425.
Altszyler, E.; Ribeiro, S.; Sigman, M.; Fernández Slezak, D. (2017). "The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text". Consciousness and Cognition. 56: 178–187. arXiv:1610.01520. doi:10.1016/j.concog.2017.09.004. PMID 28943127. S2CID 195347873.

springer.com

link.springer.com

Campello, Ricardo; Moulavi, Davoud; Sander, Joerg (2013). "Density-Based Clustering Based on Hierarchical Density Estimates". Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science. Vol. 7819. pp. 160–172. doi:10.1007/978-3-642-37456-2_14. ISBN 978-3-642-37455-5.

ssrn.com

papers.ssrn.com

Nay, John (21 December 2017). "Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text". SSRN. arXiv:1609.06616. SSRN 3087278.

worldcat.org

Von der Mosel, Julian; Trautsch, Alexander; Herbold, Steffen (2022). "On the validity of pre-trained transformers for natural language processing in the software engineering domain". IEEE Transactions on Software Engineering. 49 (4): 1487–1507. arXiv:2109.04738. doi:10.1109/TSE.2022.3178469. ISSN 1939-3520. S2CID 237485425.