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Study on text representation method based on deep learning and topic information

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Abstract

Deep learning provides a new modeling method for natural language processing. In recent years, it has been applied in language model, text classification, machine translation, sentiment analysis, question and answer system, word distributed representation, etc., and a series of theoretical research results have been obtained. For the text representation task, this paper studies the strategy of fusing global and local context information, and proposes a word representation model called Topic-based CBOW that integrates deep neural network, topic information and word order information. Then, based on the word distributed representation obtained by Topic-based CBOW, a short text representation method with TF–IWF-weighted pooling is proposed. Finally, the performance of the Topic-based CBOW model and the short text representation are compared with the baseline models, and it is found that the proposed method improves the quality of the word distributed representation to some extent by introducing the topic vector and retaining word order information, and text representation also performs well in text classification tasks.

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References

  1. Quoc L, Tomas M (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on machine learning, Beijing

  2. Bengio Y, Ducharme R, Vincent P (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155

    MATH  Google Scholar 

  3. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, Helsinki, pp 160–167

  4. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: Workshop track of the 1st international conference on learning representations, Scottsdale

  5. Mikolov T et al (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  6. Mikolov T, Karafiát M, Khudanpur S (2010) Recurrent neural network based language model. In: The 11th annual conference of the international speech communication association, Makuhari, Chiba, pp 257–264

  7. Wen Y, Zhang W, Luo R, Wang J (2016) Learning text representation using recurrent convolutional neural network with highway layers. In: Proceedings of the 39th ACM SIGIR workshop on neural information retrieval, Pisa

  8. Huang EH, Socher R, Manning CD, Ng AY (2012) Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th annual meeting of the association for computational linguistics, Jeju Island, pp 873–882

  9. Maillard J, Clark S (2015) Learning adjective meanings with a tensor-based skip-gram model. In: Nineteenth conference on computational natural language learning, Beijing, pp 327–331

  10. Zheng S, Bao H, Xu J, Hao Y et al (2016) A bidirectional hierarchical skip-gram model for text topic embedding. In: International joint conference on neural networks, Vancouver, BC, pp 855–862

  11. Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing, Doha, pp 1532–1543

  12. Peters ME, Neumann M et al (2018) Deep contextualized word representations. In: The 16th annual conference of the North American chapter of the association for computational linguistics: human language technologies, New Orleans

  13. Yan X, Guo J, Lan Y, Cheng X (2013) A biterm topic model for short texts. In: International conference on world wide web, Rio de Janeiro, pp 1445–1456

  14. Mikolov T, Sutskever I, Chen K et al (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  15. Frege G (1892) Über sinn und bedeutung. Funktion–Begriff–Bedeutung

  16. Hermann KM (2014) Distributed representations for compositional semantics. PhD thesis, University of Oxford

  17. Basili R, Moschitti A, Pazienza MT (1999) A text classifier based on linguistic processing. In: International joint conference on artificial intelligence, Stockholm, pp 1254–1266

  18. NewGroup Dataset. [EB/OL]. [2019-1-6]. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html

  19. baidu_zhidao. [EB/OL]. [2019-1-6]. http://www.datatang.com/data/39352

  20. gensim. [EB/OL]. [2019-1-6]. https://radimrehurek.com/gensim/

  21. Zhang Y et al (2018) CrossRec: cross-domain recommendations based on social big data and cognitive computing. Mob Netw Appl 23:1610–1623

    Article  Google Scholar 

  22. Zhang Y et al (2017) TempoRec: temporal-topic based recommender for social network services. Mob Netw Appl 22(6):1182–1191

    Article  Google Scholar 

  23. GloVe. [EB/OL]. [2019-1-6].https://github.com/stanfordnlp/GloVe

  24. Wang Y, Liu H (2017) SAR target discrimination based on BOW model with sample-reweighted category-specific and shared dictionary learning. IEEE Geosci Remote Sens Lett 14(11):2097–2101

    Article  Google Scholar 

  25. Moody C (2016) Mixing Dirichlet topic models and word embeddings to make lda2vec. https://arxiv.org/pdf/1605.02019

  26. lda2vec. [EB/OL]. [2019-1-6]. https://pypi.org/project/lda2vec/#files

  27. Van der Maaten L (2014) Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 15(1):3221–3245

    MathSciNet  MATH  Google Scholar 

  28. Chopra P, Yadav SK (2018) Restricted Boltzmann machine and softmax regression for fault detection and classification. Complex Intell Syst 4:67–77

    Article  Google Scholar 

  29. Harris Z (1981) Distributional structure. Word 10(23):146–162

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.71473074), the Science and Technology Program Project of Qiannan Autonomous Prefecture (No. QNKHG201713), and the Scientific Research Project of Qiannan Normal University for Nationalities (No. QNSY2017006, 2018CG010, CST-2019SN02, ML-2018KF001).

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Correspondence to Zilong Jiang.

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Jiang, Z., Gao, S. & Chen, L. Study on text representation method based on deep learning and topic information. Computing 102, 623–642 (2020). https://doi.org/10.1007/s00607-019-00755-y

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