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30.07.2021 | Original Research

A sequence labeling model for catchphrase identification from legal case documents

verfasst von: Arpan Mandal, Kripabandhu Ghosh, Saptarshi Ghosh, Sekhar Mandal

Erschienen in: Artificial Intelligence and Law | Ausgabe 3/2022

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Abstract

In a Common Law system, legal practitioners need frequent access to prior case documents that discuss relevant legal issues. Case documents are generally very lengthy, containing complex sentence structures, and reading them fully is a strenuous task even for legal practitioners. Having a concise overview of these documents can relieve legal practitioners from the task of reading the complete case statements. Legal catchphrases are (multi-word) phrases that provide a concise overview of the contents of a case document, and automated generation of catchphrases is a challenging problem in legal analytics. In this paper, we propose a novel supervised neural sequence tagging model for the extraction of catchphrases from legal case documents. Specifically, we show that incorporating document-specific information along with a sequence tagging model can enhance the performance of catchphrase extraction. We perform experiments over a set of Indian Supreme Court case documents, for which the gold-standard catchphrases (annotated by legal practitioners) are obtained from a popular legal information system. The performance of our proposed method is compared with that of several existing supervised and unsupervised methods, and our proposed method is empirically shown to be superior to all baselines.

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Fußnoten
3
Accuracy is a well-known set-based evaluation metric to measure the performance of classification algorithms, that measures what fraction of instances are correctly classified by a model. In the present context, accuracy can be used to measure what fraction of catchphrases are correctly identified by a method.
 
7
The GitHub url to our noun phrase extractor is https://​github.​com/​amarnamarpan/​NNP-extractor.
 
12
To get viterbi accuracy scores in pyCRFsuite one can use the ‘-i’ option while tagging.
 
14
available online at https://​keras.​io/​.
 
17
To compute rouge recall score we use the implementation found at https://​pypi.​org/​project/​rouge-score/​.
 
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Metadaten
Titel
A sequence labeling model for catchphrase identification from legal case documents
verfasst von
Arpan Mandal
Kripabandhu Ghosh
Saptarshi Ghosh
Sekhar Mandal
Publikationsdatum
30.07.2021
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence and Law / Ausgabe 3/2022
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI
https://doi.org/10.1007/s10506-021-09296-2