publication . Preprint . Part of book or chapter of book . 2017

Spherical Paragraph Model

Ruqing Zhang; Jiafeng Guo; Yanyan Lan; Jun Xu; Xueqi Cheng;
Open Access English
  • Published: 18 Jul 2017
Abstract
Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by distributed models, making it possible to build text representations based on a better foundation called the Bag-of-Word-Embedding (BoWE) representation. However, existing text representation methods using BoWE often lack sound probabilistic foundations or cannot well capture...
Subjects
free text keywords: Computer Science - Computation and Language, Semantics, Paragraph, Information retrieval, Probabilistic generative model, Probabilistic logic, Interpretability, Computer science, Feature learning, Semantic similarity, Sentiment analysis
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30 references, page 1 of 2

Arindam Banerjee and Sugato Basu. 2007. Topic models over text streams: A study of batch and online unsupervised learning. In SDM, volume 7, pages 437-442. SIAM. [OpenAIRE]

Arindam Banerjee, Inderjit S Dhillon, Joydeep Ghosh, and Suvrit Sra. 2005. Clustering on the unit hypersphere using von mises-fisher distributions. Journal of Machine Learning Research, 6(Sep):1345-1382.

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022.

Ste´phane Clinchant and Florent Perronnin. 2013. Aggregating continuous word embeddings for information retrieval. In Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, pages 100-109.

Scott Deerwester, Susan T Dumais, George W Furnas, Thomas K Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American society for information science, 41(6):391.

Ronald Fisher. 1953. Dispersion on a sphere. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, volume 217, pages 295-305. The Royal Society.

Zellig S Harris. 1954. Distributional structure. Word, 10(2-3):146-162.

Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016. Learning distributed representations of sentences from unlabelled data. In NAACL-HLT.

Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 50-57. ACM.

Tommi S Jaakkola, David Haussler, et al. 1999. Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, pages 487-493.

Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2):183-233.

PE Jupp and KV Mardia. 1989. A unified view of the theory of directional statistics, 1975-1988. International Statistical Review/Revue Internationale de Statistique, pages 261-294.

Yoon Kim. 2014. Convolutional neural networks for sentence classification. In EMNLP, pages 1746- 1751.

Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Skip-thought vectors. In Advances in neural information processing systems, pages 3294-3302.

Quoc V Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML, volume 14, pages 1188-1196. [OpenAIRE]

30 references, page 1 of 2
Related research
Abstract
Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semantically meaningful representations of words can be efficiently acquired by distributed models, making it possible to build text representations based on a better foundation called the Bag-of-Word-Embedding (BoWE) representation. However, existing text representation methods using BoWE often lack sound probabilistic foundations or cannot well capture...
Subjects
free text keywords: Computer Science - Computation and Language, Semantics, Paragraph, Information retrieval, Probabilistic generative model, Probabilistic logic, Interpretability, Computer science, Feature learning, Semantic similarity, Sentiment analysis
Related Organizations
Download fromView all 2 versions
http://arxiv.org/pdf/1707.0563...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book
Provider: Crossref
30 references, page 1 of 2

Arindam Banerjee and Sugato Basu. 2007. Topic models over text streams: A study of batch and online unsupervised learning. In SDM, volume 7, pages 437-442. SIAM. [OpenAIRE]

Arindam Banerjee, Inderjit S Dhillon, Joydeep Ghosh, and Suvrit Sra. 2005. Clustering on the unit hypersphere using von mises-fisher distributions. Journal of Machine Learning Research, 6(Sep):1345-1382.

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022.

Ste´phane Clinchant and Florent Perronnin. 2013. Aggregating continuous word embeddings for information retrieval. In Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, pages 100-109.

Scott Deerwester, Susan T Dumais, George W Furnas, Thomas K Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American society for information science, 41(6):391.

Ronald Fisher. 1953. Dispersion on a sphere. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, volume 217, pages 295-305. The Royal Society.

Zellig S Harris. 1954. Distributional structure. Word, 10(2-3):146-162.

Felix Hill, Kyunghyun Cho, and Anna Korhonen. 2016. Learning distributed representations of sentences from unlabelled data. In NAACL-HLT.

Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 50-57. ACM.

Tommi S Jaakkola, David Haussler, et al. 1999. Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, pages 487-493.

Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2):183-233.

PE Jupp and KV Mardia. 1989. A unified view of the theory of directional statistics, 1975-1988. International Statistical Review/Revue Internationale de Statistique, pages 261-294.

Yoon Kim. 2014. Convolutional neural networks for sentence classification. In EMNLP, pages 1746- 1751.

Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Skip-thought vectors. In Advances in neural information processing systems, pages 3294-3302.

Quoc V Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML, volume 14, pages 1188-1196. [OpenAIRE]

30 references, page 1 of 2
Related research
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publication . Preprint . Part of book or chapter of book . 2017

Spherical Paragraph Model

Ruqing Zhang; Jiafeng Guo; Yanyan Lan; Jun Xu; Xueqi Cheng;