Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality

Md Shad Akhtar; Palaash Sawant; Sukanta Sen; Asif Ekbal; Pushpak Bhattacharyya

doi:10.18653/v1/N18-1053

Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality

Md Shad Akhtar, Palaash Sawant, Sukanta Sen, Asif Ekbal, Pushpak Bhattacharyya

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

Abstract

Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensified in resource-poor scenario due to the absence of sufficient amount of corpus. In this work we propose to minimize the effect of data sparsity by leveraging bilingual word embeddings learned through a parallel corpus. We train and evaluate Long Short Term Memory (LSTM) based architecture for aspect level sentiment classification. The neural network architecture is further assisted by the hand-crafted features for the prediction. We show the efficacy of the proposed model against state-of-the-art methods in two experimental setups i.e. multi-lingual and cross-lingual.

Original language	English
Title of host publication	Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Place of Publication	New Orleans, Louisiana
Publisher	Association for Computational Linguistics
Pages	572-582
Number of pages	11
ISBN (Electronic)	978-1-948087-27-8
DOIs	https://doi.org/10.18653/v1/N18-1053
Publication status	Published - 1 Jun 2018
Event	16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Hyatt Regency New Orleans Hotel, New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 http://naacl2018.org/

Conference

Conference	16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated title	NAACL HLT 2018
Country/Territory	United States
City	New Orleans
Period	1/06/18 → 6/06/18
Internet address	http://naacl2018.org/

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Solving Data Sparsity_AKHTAR_DOA13022018_VOR_CC-BYFinal published version, 427 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/N18-1053Licence: Creative Commons: Attribution (CC-BY)

Cite this

Akhtar, M. S., Sawant, P., Sen, S., Ekbal, A., & Bhattacharyya, P. (2018). Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 572-582). Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1053

Akhtar, Md Shad ; Sawant, Palaash ; Sen, Sukanta et al. / Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana : Association for Computational Linguistics, 2018. pp. 572-582

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abstract = "Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great challenge in creating efficient word representation model for solving the underlying problem. The problem is more intensified in resource-poor scenario due to the absence of sufficient amount of corpus. In this work we propose to minimize the effect of data sparsity by leveraging bilingual word embeddings learned through a parallel corpus. We train and evaluate Long Short Term Memory (LSTM) based architecture for aspect level sentiment classification. The neural network architecture is further assisted by the hand-crafted features for the prediction. We show the efficacy of the proposed model against state-of-the-art methods in two experimental setups i.e. multi-lingual and cross-lingual.",

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Akhtar, MS, Sawant, P, Sen, S, Ekbal, A & Bhattacharyya, P 2018, Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality. in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, Louisiana, pp. 572-582, 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana, United States, 1/06/18. https://doi.org/10.18653/v1/N18-1053

Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality. / Akhtar, Md Shad; Sawant, Palaash; Sen, Sukanta et al.

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana : Association for Computational Linguistics, 2018. p. 572-582.

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

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AU - Bhattacharyya, Pushpak

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Akhtar MS, Sawant P, Sen S, Ekbal A, Bhattacharyya P. Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana: Association for Computational Linguistics. 2018. p. 572-582 doi: 10.18653/v1/N18-1053

Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality

Abstract

Conference

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