Reinforcing the topic of embeddings with Theta Pure Dependence for text classification

Ning Xing; Yuexian Hou; Peng Zhang; Wenjie Li; Dawei Song

doi:10.18653/v1/d15-1305

Reinforcing the topic of embeddings with Theta Pure Dependence for text classification

Ning Xing, Yuexian Hou^*, Peng Zhang, Wenjie Li, Dawei Song

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

For sentiment classification, it is often recognized that embedding based on distributional hypothesis is weak in capturing sentiment contrast-contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce topical and sentimental information. TPD has a theoretical guarantee that the word dependency is pure, i.e., the dependence pattern has the integral meaning whose underlying distribution can not be conditionally factorized. Our method outperforms the state-of-the-art performance on text classification tasks.

Original language	English
Title of host publication	Conference Proceedings - EMNLP 2015
Subtitle of host publication	Conference on Empirical Methods in Natural Language Processing
Publisher	Association for Computational Linguistics (ACL)
Pages	2551-2556
Number of pages	6
ISBN (Electronic)	9781941643327
DOIs	https://doi.org/10.18653/v1/d15-1305
Publication status	Published - 2015
Externally published	Yes
Event	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Lisbon, Portugal Duration: 17 Sept 2015 → 21 Sept 2015

Publication series

Name	Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

Conference

Conference	Conference on Empirical Methods in Natural Language Processing, EMNLP 2015
Country/Territory	Portugal
City	Lisbon
Period	17/09/15 → 21/09/15

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10.18653/v1/d15-1305

Cite this

Xing, N., Hou, Y., Zhang, P., Li, W., & Song, D. (2015). Reinforcing the topic of embeddings with Theta Pure Dependence for text classification. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing (pp. 2551-2556). (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d15-1305

Xing, Ning ; Hou, Yuexian ; Zhang, Peng et al. / Reinforcing the topic of embeddings with Theta Pure Dependence for text classification. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. pp. 2551-2556 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

@inproceedings{c25b7465d68049a9863e541a4dc66df5,

title = "Reinforcing the topic of embeddings with Theta Pure Dependence for text classification",

abstract = "For sentiment classification, it is often recognized that embedding based on distributional hypothesis is weak in capturing sentiment contrast-contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce topical and sentimental information. TPD has a theoretical guarantee that the word dependency is pure, i.e., the dependence pattern has the integral meaning whose underlying distribution can not be conditionally factorized. Our method outperforms the state-of-the-art performance on text classification tasks.",

author = "Ning Xing and Yuexian Hou and Peng Zhang and Wenjie Li and Dawei Song",

note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 ; Conference date: 17-09-2015 Through 21-09-2015",

year = "2015",

doi = "10.18653/v1/d15-1305",

language = "English",

series = "Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics (ACL)",

pages = "2551--2556",

booktitle = "Conference Proceedings - EMNLP 2015",

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}

Xing, N, Hou, Y, Zhang, P, Li, W & Song, D 2015, Reinforcing the topic of embeddings with Theta Pure Dependence for text classification. in Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics (ACL), pp. 2551-2556, Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17/09/15. https://doi.org/10.18653/v1/d15-1305

Reinforcing the topic of embeddings with Theta Pure Dependence for text classification. / Xing, Ning; Hou, Yuexian; Zhang, Peng et al.
Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), 2015. p. 2551-2556 (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hou, Yuexian

AU - Zhang, Peng

AU - Li, Wenjie

AU - Song, Dawei

PY - 2015

Y1 - 2015

N2 - For sentiment classification, it is often recognized that embedding based on distributional hypothesis is weak in capturing sentiment contrast-contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce topical and sentimental information. TPD has a theoretical guarantee that the word dependency is pure, i.e., the dependence pattern has the integral meaning whose underlying distribution can not be conditionally factorized. Our method outperforms the state-of-the-art performance on text classification tasks.

AB - For sentiment classification, it is often recognized that embedding based on distributional hypothesis is weak in capturing sentiment contrast-contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce topical and sentimental information. TPD has a theoretical guarantee that the word dependency is pure, i.e., the dependence pattern has the integral meaning whose underlying distribution can not be conditionally factorized. Our method outperforms the state-of-the-art performance on text classification tasks.

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U2 - 10.18653/v1/d15-1305

DO - 10.18653/v1/d15-1305

M3 - Conference contribution

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T3 - Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

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BT - Conference Proceedings - EMNLP 2015

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T2 - Conference on Empirical Methods in Natural Language Processing, EMNLP 2015

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ER -

Xing N, Hou Y, Zhang P, Li W, Song D. Reinforcing the topic of embeddings with Theta Pure Dependence for text classification. In Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL). 2015. p. 2551-2556. (Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing). doi: 10.18653/v1/d15-1305

Reinforcing the topic of embeddings with Theta Pure Dependence for text classification

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