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Effective Seed-Guided Topic Labeling for Dataless Hierarchical Short Text Classification

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Web Engineering (ICWE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12706))

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Abstract

Hierarchical text classification has a wide application prospect on the Internet, which aims to classify texts into a given hierarchy. Supervised methods require a large amount of labeled data and are thus costly. For this purpose, the task of dataless hierarchical text classification has attracted more and more attention of researchers in recent years, which only requires a few relevant seed words for given categories. However, existing approaches mainly focus on long texts without considering the characteristics of short texts, so are not suitable in many scenarios. In this paper, we tackle dataless hierarchical short text classification for the first time, and propose an innovative model named Hierarchical Seeded Biterm Topic Model (HierSeedBTM), which effectively leverages seed words in Biterm Topic Model (BTM) to guide the hierarchical topic labeling. Specifically, our model introduces iterative distribution propagation mechanism among topic models in different levels to incorporate the hierarchical structure information. Experiments on two public datasets show that the proposed model is more effective than the state-of-the-art methods of dataless hierarchical text classification designed for long texts.

This work is supported by National Key Research and Development Program of China (2018YFC0116703), Strategic Priority Research Program of Chinese Academy of Sciences (XDC02060500), and Zhejiang Lab (2020NF0AC02).

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Notes

  1. 1.

    http://qwone.com/jason/20Newsgroups/.

  2. 2.

    https://www.kaggle.com/rmisra/news-category-dataset.

  3. 3.

    https://github.com/CogComp/cogcomp-nlp/tree/master/dataless-classifier.

  4. 4.

    https://github.com/yumeng5/WeSHClass.

  5. 5.

    https://github.com/HKUST-KnowComp/PathPredictionForTextClassification.

  6. 6.

    http://nlp.stanford.edu/data/glove.840B.300d.zip.

References

  1. Bennett, P., Nguyen, N.: Refined experts: improving classification in large taxonomies. In: SIGIR, pp. 11–18. ACM (2009)

    Google Scholar 

  2. Chen, H., Dumais, S.T.: Bringing order to the web: automatically categorizing search results. In: CHI, pp. 145–152. ACM (2000)

    Google Scholar 

  3. Chen, W., Wang, J., Zhang, Y., Yan, H., Li, X.: User based aggregation for biterm topic model. In: ACL, vol. 2 (Short Papers), pp. 489–494 (2015)

    Google Scholar 

  4. Chen, X., Xia, Y., Jin, P., Carroll, J.: Dataless text classification with descriptive LDA. In: AAAI, pp. 2224–2231 (2015)

    Google Scholar 

  5. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171–4186. Association for Computational Linguistics (2019)

    Google Scholar 

  6. Druck, G., Mann, G., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: SIGIR, pp. 595–602. ACM (2008)

    Google Scholar 

  7. Dumais, S.T., Chen, H.: Hierarchical classification of web content. In: SIGIR, pp. 256–263. ACM (2000)

    Google Scholar 

  8. Jiang, L., Lu, H., Xu, M., Wang, C.: Biterm pseudo document topic model for short text. In: ICTAI, pp. 865–872. IEEE (2016)

    Google Scholar 

  9. Koller, D., Sahami, M.: Hierarchically classifying documents using very few words. In: ICML, pp. 170–178. Morgan Kaufmann (1997)

    Google Scholar 

  10. Lang, K.: Newsweeder: learning to filter netnews. In: ICML, pp. 331–339. Morgan Kaufmann (1995)

    Google Scholar 

  11. Li, C., Chen, S., Qi, Y.: Filtering and classifying relevant short text with a few seed words. Data Inf. Manag. 3(3), 165–186 (2019)

    Google Scholar 

  12. Li, C., Chen, S., Xing, J., Sun, A., Ma, Z.: Seed-guided topic model for document filtering and classification. ACM Trans. Inf. Syst. 37(1), 9:1–9:37 (2019)

    Article  Google Scholar 

  13. Li, C., Xing, J., Sun, A., Ma, Z.: Effective document labeling with very few seed words: a topic model approach. In: CIKM, pp. 85–94. ACM (2016)

    Google Scholar 

  14. Li, X., Zhang, A., Li, C., Guo, L., Wang, W., Ouyang, J.: Relational biterm topic model: short-text topic modeling using word embeddings. Comput. J. 62(3), 359–372 (2018)

    Article  Google Scholar 

  15. Liu, B., Li, X., Lee, W.S., Yu, P.S.: Text classification by labeling words. In: AAAI, vol. 4, pp. 425–430 (2004)

    Google Scholar 

  16. Mekala, D., Shang, J.: Contextualized weak supervision for text classification. In: ACL, pp. 323–333. Association for Computational Linguistics (2020)

    Google Scholar 

  17. Meng, Y., Shen, J., Zhang, C., Han, J.: Weakly-supervised neural text classification. In: CIKM, pp. 983–992. ACM (2018)

    Google Scholar 

  18. Meng, Y., Shen, J., Zhang, C., Han, J.: Weakly-supervised hierarchical text classification, vol. 33, no. 01, pp. 6826–6833 (2019)

    Google Scholar 

  19. Misra, R.: News category dataset (2018). https://doi.org/10.13140/RG.2.2.20331.18729

  20. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: EMNLP, pp. 79–86 (2002)

    Google Scholar 

  21. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543. Association for Computational Linguistics (2014)

    Google Scholar 

  22. Quan, X., Kit, C., Ge, Y., Pan, S.J.: Short and sparse text topic modeling via self-aggregation. In: IJCAI, pp. 2270–2276 (2015)

    Google Scholar 

  23. Song, Y., Roth, D.: On dataless hierarchical text classification. In: AAAI, pp. 1579–1585. AAAI Press (2014)

    Google Scholar 

  24. Tang, D., Qin, B., Liu, T.: EMNLP, pp. 1422–1432. The Association for Computational Linguistics (2015)

    Google Scholar 

  25. Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential Twitterers. In: WSDM, pp. 261–270. ACM (2010)

    Google Scholar 

  26. Xiao, H., Liu, X., Song, Y.: Efficient path prediction for semi-supervised and weakly supervised hierarchical text classification. In: WWW, pp. 3370–3376. ACM (2019)

    Google Scholar 

  27. Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: WWW, pp. 1445–1456. ACM (2013)

    Google Scholar 

  28. Yang, Y., et al.: Dataless short text classification based on biterm topic model and word embeddings. In: Bessiere, C. (ed.) International Joint Conferences on Artificial Intelligence Organization, IJCAI, pp. 3969–3975 (2020)

    Google Scholar 

  29. Yin, J., Wang, J.: A Dirichlet multinomial mixture model-based approach for short text clustering. In: SIGKDD, pp. 233–242. ACM (2014)

    Google Scholar 

  30. Zhao, W.X., et al.: Comparing twitter and traditional media using topic models. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20161-5_34

    Chapter  Google Scholar 

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Authors and Affiliations

  1. SKLCS, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Yi Yang, Hongan Wang, Jiaqi Zhu, Wandong Shi, Wenli Guo & Jiawen Zhang

  2. Zhejiang Lab, Hangzhou, Zhejiang, China

    Jiaqi Zhu

  3. University of Chinese Academy of Sciences, Beijing, China

    Yi Yang, Hongan Wang, Jiaqi Zhu, Wandong Shi & Jiawen Zhang

Authors
  1. Yi Yang
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  2. Hongan Wang
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  3. Jiaqi Zhu
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  4. Wandong Shi
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  5. Wenli Guo
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  6. Jiawen Zhang
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Corresponding author

Correspondence to Jiaqi Zhu .

Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy

    Marco Brambilla

  2. E2S UPPA, LIUPPA, Université de Pau et des Pays de l’Adour, Anglet, France

    Richard Chbeir

  3. Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  4. Inria Saclay-ÃŽle-de-France, Institut Polytechnique de Paris, Palaiseau, France

    Ioana Manolescu

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Yang, Y., Wang, H., Zhu, J., Shi, W., Guo, W., Zhang, J. (2021). Effective Seed-Guided Topic Labeling for Dataless Hierarchical Short Text Classification. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-74296-6_21

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