nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Aspect Term Extraction Using Deep Learning Model with Minimal Feature Engineering

verfasst von : Felipe Zschornack Rodrigues Saraiva, Ticiana Linhares Coelho da Silva, José Antônio Fernandes de Macêdo

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

With the explosive growth of social media on the Web, opinion mining has been extensively investigated and consists of the automatic identification and extraction of opinions, emotions, and sentiments from text and multimedia data. One of the tasks involved in opinion mining is Aspect Term Extraction (ATE) which aims at identifying aspects (attributes or characteristics) that have been explicitly evaluated in a sentence or a document. For example, in the sentence “The picture quality of this camera is amazing”, the aspect term is “picture quality”. This work proposes POS-AttWD-BLSTM-CRF, a neural network architecture using a deep learning model, and minimal feature engineering, to solve the problem of ATE in opinionated documents. The proposed architecture consists of a BLSTM-CRF classifier that uses the part-of-speech tag (POS tags) as an additional feature, along with a BLSTM encoder with an attention mechanism to allow the incorporation of another relevant feature: the grammatical relations between words. The experiments show that the proposed architecture achieves promising results with minimal feature engineering comparing to the state-of-the-art solutions.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Online Reinforcement Learning for Self-adaptive Information Systems

Nächstes Kapitel State Machine Based Human-Bot Conversation Model and Services

http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools.

http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools.

Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

Bharadwaj, A., Mortensen, D., Dyer, C., Carbonell, J.: Phonologically aware neural model for named entity recognition in low resource transfer settings. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1462–1472 (2016)

Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)CrossRef

Chernyshevich, M.: IHS R&D belarus: cross-domain extraction of product features using CRF. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 309–313 (2014)

Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

De Marneffe, M.C., Manning, C.D.: Stanford typed dependencies manual. Technical report, Stanford University (2008)

Do, H.H., Prasad, P., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019)CrossRef

Giannakopoulos, A., Musat, C., Hossmann, A., Baeriswyl, M.: Unsupervised aspect term extraction with B-LSTM & CRF using automatically labelled datasets. arXiv preprint arXiv:1709.05094 (2017)

Graves, A., Fernández, S., Schmidhuber, J.: Bidirectional LSTM networks for improved phoneme classification and recognition. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 799–804. Springer, Heidelberg (2005). https://doi.org/10.1007/11550907_126CrossRef

10.

Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)

11.

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

12.

Lakkaraju, H., Socher, R., Manning, C.: Aspect specific sentiment analysis using hierarchical deep learning. In: NIPS Workshop on Deep Learning and Representation Learning (2014)

13.

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)

14.

Li, X., Bing, L., Li, P., Lam, W., Yang, Z.: Aspect term extraction with history attention and selective transformation. arXiv preprint arXiv:1805.00760 (2018)

15.

Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_13CrossRef

16.

Luo, L., et al.: An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics 34(8), 1381–1388 (2017)CrossRef

17.

Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)

18.

Ma, Y., Peng, H., Cambria, E.: Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

19.

Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162

20.

Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)CrossRef

21.

Rana, T.A., Cheah, Y.N.: Aspect extraction in sentiment analysis: comparative analysis and survey. Artif. Intell. Rev. 46(4), 459–483 (2016)CrossRef

22.

Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)

23.

Shu, L., Xu, H., Liu, B.: Controlled CNN-based sequence labeling for aspect extraction. arXiv preprint arXiv:1905.06407 (2019)

24.

Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

25.

Thein, K.M.M., Nyunt, T.T.S.: Apache Kafka: a high-throughput distributed messaging system. In: Thirteenth International Conference On Computer Applications (ICCA 2015) (2015)

26.

Toh, Z., Su, J.: NLANGP at SemEval-2016 task 5: improving aspect based sentiment analysis using neural network features. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), pp. 282–288 (2016)

27.

Toh, Z., Wang, W.: DLIREC: aspect term extraction and term polarity classification system. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 235–240 (2014)

28.

Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics (2003)

29.

Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint arXiv:1603.06679 (2016)

30.

Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

31.

Xie, J., Yang, Z., Neubig, G., Smith, N.A., Carbonell, J.: Neural cross-lingual named entity recognition with minimal resources. arXiv preprint arXiv:1808.09861 (2018)

32.

Xu, H., Liu, B., Shu, L., Yu, P.S.: Double embeddings and CNN-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601 (2018)

Titel: Aspect Term Extraction Using Deep Learning Model with Minimal Feature Engineering
verfasst von: Felipe Zschornack Rodrigues Saraiva
Ticiana Linhares Coelho da Silva
José Antônio Fernandes de Macêdo
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-030-49434-6

Electronic ISBN: 978-3-030-49435-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49435-3_12

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner