Statistical Recognition of References in Czech Court Decisions

Kríž, Vincent; Hladká, Barbora; Dědek, Jan; Nečaský, Martin

doi:10.1007/978-3-319-13647-9_6

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Legal text is one specialist domain where NER is of interest, due to its usefulness in assisting other legal tasks such as record linkage (Dozier et al, 2010), court case linkage (Kríž et al, 2014), contract analysis (Chalkidis et al, 2017), prediction of judicial decisions (Aletras et al, 2016), credit risk assessment (Alvarado et al, 2015), and questionanswering systems (Jayakumar et al, 2020). However, despite increasing interest in this sub-domain, there is no publicly available corpus for the evaluation of NER methods for legal applications.…”

Section: Introductionmentioning

confidence: 99%

E-NER — An Annotated Named Entity Recognition Corpus of Legal Text

Terence¹,

Lampos²,

Cox³

2022

Proceedings of the Natural Legal Language Processing Workshop 2022

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Identifying named entities such as a person, location or organization, in documents can highlight key information to readers. Training Named Entity Recognition (NER) models requires an annotated data set, which can be a time-consuming labour-intensive task. Nevertheless, there are publicly available NER data sets for general English. Recently there has been interest in developing NER for legal text. However, prior work and experimental results reported here indicate that there is a significant degradation in performance when NER methods trained on a general English data set are applied to legal text. We describe a publicly available legal NER data set, called E-NER, based on legal company filings available from the US Securities and Exchange Commission's EDGAR data set. Training a number of different NER algorithms on the general English CoNLL-2003 corpus but testing on our test collection confirmed significant degradations in accuracy, as measured by the F1-score, of between 29.4% and 60.4%, compared to training and testing on the E-NER collection.

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Section: Introductionmentioning

confidence: 99%

E-NER — An Annotated Named Entity Recognition Corpus of Legal Text

Terence¹,

Lampos²,

Cox³

2022

Proceedings of the Natural Legal Language Processing Workshop 2022

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Annotated Corpus of Czech Case Law for Reference Recognition Tasks

Harašta

Šavelka

Kasl

et al. 2018

Text, Speech, and Dialogue

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Document Similarity of Czech Supreme Court Decisions

Novotná

2020

MUJLT

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Retrieval of court decisions dealing with a similar legal matter is a prevalent task performed by lawyers as it is a part of a relevant decision-making practice review. In spite of the natural language processing methods that are currently available, this legal research is still mostly done through Boolean searches or by contextual retrieval. In this study, it is experimentally verified whether the doc2vec method together with cosine similarity, can automatically retrieve the Czech Supreme Court decisions dealing with a similar legal issue as a given decision. Furthermore, the limits and challenges of these methods and its application on the Czech Supreme Court decisions are discussed.

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Statistical Recognition of References in Czech Court Decisions

Cited by 3 publications

References 13 publications

E-NER — An Annotated Named Entity Recognition Corpus of Legal Text

E-NER — An Annotated Named Entity Recognition Corpus of Legal Text

Annotated Corpus of Czech Case Law for Reference Recognition Tasks

Document Similarity of Czech Supreme Court Decisions

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