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International Conference on Theory and Practice of Digital Libraries

TPDL 2019: Digital Libraries for Open Knowledge pp 415–419Cite as

Topic Modelling vs Distant Supervision: A Comparative Evaluation Based on the Classification of Parliamentary Enquiries

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11799))

Abstract

We investigate two different approaches to text classification, by categorising enquiries submitted to the House of Commons Library from elected Members of the UK Parliament. One is an unsupervised approach, i.e. topic modelling, and the other is a supervised approach based on weakly labelled data, i.e. distant supervision. Models were trained on two types of feature sets: one based only on bag of words, and the other combining bag of words with structured metadata attached to enquiries. Our results show that topic modelling obtains superior performance on this task, and that the incorporation of structured metadata as learning features contributes insignificantly to improved model performance.

Supported by an EPSRC Impact Acceleration Account awarded to the University of Manchester.

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Notes

  1. 1.

    There are eight research sections in the Library: Business and Transport Section (BTS), Economic Policy and Statistics (EPAS), Home Affairs Section (HAS), International Affairs and Defence Section (IADS), Parliament and Constitution Centre (PCC), Science and Environment Section (SES), Social and General Statistics (SGS), Social Policy Section (SPS).

  2. 2.

    https://www.parliament.uk/topics/topical-issues.htm.

  3. 3.

    http://www.structuraltopicmodel.com/.

  4. 4.

    http://topepo.github.io/caret/index.html.

  5. 5.

    Each enquiry was assigned at most two labels or topics from the taxonomy.

  6. 6.

    The political affiliation of the MP’s office at the time they submitted an enquiry, which was obtained from the UK Parliament’s data platform using the pdpr R package: https://github.com/olihawkins/pdpr.

References

  1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

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  2. Joachims, T.: Text categorization with Support Vector Machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) Machine Learning: ECML 1998. LNCS, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683

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  3. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2, ACL 2009, pp. 1003–1011. Association for Computational Linguistics, Stroudsburg (2009)

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  4. Roberts, M.E., Stewart, B.M., Tingley, D., et al.: STM: R package for structural topic models. J. Stat. Softw. 10, 1–40 (2014)

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Manchester, Manchester, UK

    Riza Batista-Navarro

  2. House of Commons Library, Parliament, London, UK

    Oliver Hawkins

Authors
  1. Riza Batista-Navarro
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  2. Oliver Hawkins
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Corresponding author

Correspondence to Riza Batista-Navarro .

Editor information

Editors and Affiliations

  1. University of La Rochelle, La Rochelle, France

    Antoine Doucet

  2. VU University Amsterdam, Amsterdam, The Netherlands

    Antoine Isaac

  3. Linnaeus University, Växjö, Sweden

    Koraljka Golub

  4. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Trond Aalberg

  5. Kyoto University, Kyoto, Japan

    Adam Jatowt

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Batista-Navarro, R., Hawkins, O. (2019). Topic Modelling vs Distant Supervision: A Comparative Evaluation Based on the Classification of Parliamentary Enquiries. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_46

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  • DOI: https://doi.org/10.1007/978-3-030-30760-8_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30759-2

  • Online ISBN: 978-3-030-30760-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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