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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Active Learning Enhanced Sequence Labeling for Aspect Term Extraction in Review Data

verfasst von : K. Shyam Sundar, Deepa Gupta

Erschienen in: Advanced Computing

Verlag: Springer Singapore

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Abstract

Analyzing reviews with respect to each aspect gives better understanding as compared to overall opinions and this requires the aspect terms and their corresponding opinions to be extracted. Supervised models for aspect term extraction require large amount of labeled data. Aspect annotated data is scarcely available for use and the cost of manual annotation of the entire data is huge. This study proposes a way of using Active Learning to select a highly informative subset of the data that needs to be labeled, to train the supervised model. The identification of aspect terms is defined as a sequence labelling problem with the help of BiLSTM network and CRF. The model is trained on publicly available SemEval (2014–16) datasets for restaurant and laptop reviews. The results show a 36% and 42% reduction in annotation cost for restaurants and laptops respectively, with negligible effect on the model’s performance. A significant difference in cost is observed between active learning guided sampling and random sampling approaches.

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Vorheriges Kapitel Misogynous Text Classification Using SVM and LSTM
Nächstes Kapitel Intuitive Feature Engineering and Machine Learning Performance Improvement in the Banking Domain
Literatur
1.
2.
Pavlopoulos, J., Androustsopoulos, I.: Aspect term extraction for sentiment analysis: new datasets, new evaluation measures and improved unsupervised method. In: Proceedings of the 5th Workshop on Language Analysis for Social Media, pp. 44–52. Association for Computational Linguistics, Gothenburg (2014)
3.
Kholgi, M.: Active learning for concept extraction from clinical free text. Ph.D. thesis, Queensland University of Technology (2017)
4.
Vu, V., Labroche, N.: Active seed selection for constrained clustering. Intell. Syst. 21, 537–552 (2017)
5.
6.
Sutton, R.S., Barto, A.G.: Reinforcement Learning, 2nd edn. MIT Press, London (2018)MATH
7.
8.
Liu, B., Hu, M.: Opinion mining, sentiment analysis, and opinion spam detection dataset (2004)
9.
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL, Vancouver, pp. 388–397 (2017)
10.
Luo, L., et al.: Unsupervised neural aspect extraction with sememes. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI, Macao, China, pp. 5123–5129 (2019)
11.
Chauhan, G.S., et al.: An unsupervised multiple word-embedding method with attention model for cross domain aspect term extraction. In: 3rd International Conference on Emerging Technologies in Computer Engineering, pp. 110–116. IEEE, Jaipur (2020)
12.
Giannakopoulos, A., et al.: Unsupervised aspect term extraction with Bi-LSTM & CRF using automatically labeled datasets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 180–188. Association for Computational Linguistics, Copenhagen (2017)
13.
Fu, X., et al.: Semi-supervised aspect-level sentiment classification model based on variational autoencoder. Knowl. Based Syst. 171, 81–92 (2019)CrossRef
14.
15.
Xiang, Y., He, H., Zheng, J.: Aspect term extraction based on MFE-CRF. Information 9, 198 (2018)CrossRef
16.
Li, X., et al.: Aspect term extraction with history attention and selective transformation. In: Proceedings of 27th International Joint Conference on Artificial Intelligence, IJCAI, Stockholm, Sweden, pp. 4194–4200 (2018)
17.
Dalal, H., Gao, G.: Aspect extraction from reviews using conditional random fields. In: The Sixth International Conference on Data Analytics, pp. 158–167, Data Analytics, Barcelona, Spain (2015)
18.
Cahyadi, A., Khodra, M.L.: Aspect-based sentiment analysis using convolution neural networks and bidirectional long short-term memory. In: Proceedings of the 5th International Conference on Advanced Informatics: Concept Theory and Applications, pp. 124–129. IEEE, Krabi (2018)
19.
Dai, H.L., Song, Y.Q.: Neural aspect and opinion term extraction with mined rules as weak supervision. In: Proceedings of the 57th Annual Meeting of Association for Computational Linguistics, ACL, Florence, Italy, pp. 5268–5277 (2019)
20.
Query ID="Q4" Text="Kindly provide the page range for Ref. [20], if possible." Ray, P., Chakrabarti, A.: A mixed approach of deep learning method and rule-based method to improve aspect level sentiment analysis. Appl. Comput. Inform. 15(1) (2019)
21.
Ning, L., Bo, S.: Aspect-based sentiment analysis with gated alternate neural network. Knowl.-Based Syst. 188, 105010 (2019)
22.
Augustyniak, L., Kajdanowicz, T., Kazienko, P.: Comprehensive analysis of aspect term extraction methods using various text embeddings. arXiv (2019)
23.
Venugopalan, M., Gupta, D.: An unsupervised hierarchical rule based model for aspect term extraction augmented with pruning strategies. Procedia Comput. Sci. 171, 22–31 (2020)CrossRef
24.
Narasimhan, K., Yala, A., Barzilay, R.: Improving information extraction by acquiring external evidence with reinforcement learning. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2355–2365. ACL, Austin (2016)
25.
Meng, F., Yuan, L., Cohn, T.: Learning how to active learn: a deep reinforcement learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 595–605. ACL, Copenhagen (2017)
26.
Diligach, D., Palmer, M.: Good seed makes a good crop: accelerating active learning using language modelling. In: Proceedings of 49th Annual Meeting of the Association of Computational Linguistics, pp. 6–10. ACM, Portland (2011)
27.
Chairi, I., Alaoui, S., Lyhyaouier, A.: Sample selection based active learning for imbalanced data. In: Proceedings of the 10th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 645–651. IEEE, Marrakech (2014)
28.
Yang, B., et al.: Effective multi-label active learning for text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 917–926. ACM, Paris (2009)
29.
Li, H.: Deep learning for natural language processing: advantages and challenges. Natl. Sci. Rev. 5(1), 24–26 (2017)CrossRef
30.
31.
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D: Nonlinear Phenom. 404, 132306 (2020)MathSciNetCrossRef
32.
Greff, K., et al.: LSTM: a search space odessy. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017)MathSciNetCrossRef
33.
Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648. University of Wisconsin–Madison (2009)
34.
Settles, B., Craven, M., Ray, S.: Multiple-instance active learning. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, pp. 1289–1296. Curran Associated Inc., Red Hook (2007)
35.
36.
Metadaten
Titel
Active Learning Enhanced Sequence Labeling for Aspect Term Extraction in Review Data
verfasst von
K. Shyam Sundar
Deepa Gupta
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Advanced Computing

Print ISBN: 978-981-16-0400-3

Electronic ISBN: 978-981-16-0401-0

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-16-0401-0_27

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