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Legal Information as a Complex Network: Improving Topic Modeling Through Homophily

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

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Abstract

Topic modeling is a key component to computational legal science. Network analysis is also very important to further understand the structure of references in legal documents. In this paper, we improve topic modeling for legal case documents by using homophily networks derived from two families of references: prior cases and statute laws. We perform a detailed analysis on a rich legal case dataset in order to create these networks. The use of the reference-induced homophily topic modeling improves on prior methods.

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Notes

  1. 1.

    https://sites.ualberta.ca/~miyoung2/COLIEE2018/.

  2. 2.

    https://gist.github.com/sebleier/554280.

  3. 3.

    https://github.com/dongwookim-ml/python-topic-model/blob/master/notebook/RelationalTopicModel_example.ipynb.

  4. 4.

    https://github.com/dongwookim-ml/python-topic-model.

  5. 5.

    https://radimrehurek.com/gensim/models/ldamodel.html.

  6. 6.

    https://nlp.stanford.edu/projects/glove/. We focused on the top ten words output by a topic model and evaluated its performance.

  7. 7.

    https://figshare.com/articles/Legal_Information_as_a_Complex_Network_Improving_Topic_Modeling_through_Homophily/9724070.

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Authors and Affiliations

  1. Graduate of Information Science and Technology, Institute for Datability Science, Graduate School of Law and Politics, Osaka University, Osaka, Japan

    Kazuki Ashihara, Chenhui Chu, Benjamin Renoust, Noriko Okubo, Noriko Takemura, Yuta Nakashima & Hajime Nagahara

Authors
  1. Kazuki Ashihara
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  2. Chenhui Chu
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  3. Benjamin Renoust
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  4. Noriko Okubo
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  5. Noriko Takemura
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  6. Yuta Nakashima
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  7. Hajime Nagahara
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Corresponding author

Correspondence to Kazuki Ashihara .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Ashihara, K. et al. (2020). Legal Information as a Complex Network: Improving Topic Modeling Through Homophily. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_3

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