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        <title>Efficient Deep Learning-based Sentence Boundary Detection in Legal Text</title>
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        <namePart type="given">Reshma</namePart>
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        <namePart type="given">Gokul</namePart>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2022</title>
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            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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        <name type="personal">
            <namePart type="given">Ilias</namePart>
            <namePart type="family">Chalkidis</namePart>
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            <namePart type="given">Leslie</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>A key component of the Natural Language Processing (NLP) pipeline is Sentence Boundary Detection (SBD). Erroneous SBD could affect other processing steps and reduce performance. A few criteria based on punctuation and capitalization are necessary to identify sentence borders in well-defined corpora. However, due to several grammatical ambiguities, the complex structure of legal data poses difficulties for SBD. In this paper, we have trained a neural network framework for identifying the end of the sentence in legal text. We used several state-of-the-art deep learning models, analyzed their performance, and identified that Convolutional Neural Network(CNN) outperformed other deep learning frameworks. We compared the results with rule-based, statistical, and transformer-based frameworks. The best neural network model outscored the popular rule-based framework with an improvement of 8% in the F1 score. Although domain-specific statistical models have slightly improved performance, the trained CNN is 80 times faster in run-time and doesn’t require much feature engineering. Furthermore, after extensive pretraining, the transformer models fall short in overall performance compared to the best deep learning model.</abstract>
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    <identifier type="doi">10.18653/v1/2022.nllp-1.18</identifier>
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%0 Conference Proceedings
%T Efficient Deep Learning-based Sentence Boundary Detection in Legal Text
%A Sheik, Reshma
%A T, Gokul
%A Nirmala, S.
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F sheik-etal-2022-efficient
%X A key component of the Natural Language Processing (NLP) pipeline is Sentence Boundary Detection (SBD). Erroneous SBD could affect other processing steps and reduce performance. A few criteria based on punctuation and capitalization are necessary to identify sentence borders in well-defined corpora. However, due to several grammatical ambiguities, the complex structure of legal data poses difficulties for SBD. In this paper, we have trained a neural network framework for identifying the end of the sentence in legal text. We used several state-of-the-art deep learning models, analyzed their performance, and identified that Convolutional Neural Network(CNN) outperformed other deep learning frameworks. We compared the results with rule-based, statistical, and transformer-based frameworks. The best neural network model outscored the popular rule-based framework with an improvement of 8% in the F1 score. Although domain-specific statistical models have slightly improved performance, the trained CNN is 80 times faster in run-time and doesn’t require much feature engineering. Furthermore, after extensive pretraining, the transformer models fall short in overall performance compared to the best deep learning model.
%R 10.18653/v1/2022.nllp-1.18
%U https://aclanthology.org/2022.nllp-1.18
%U https://doi.org/10.18653/v1/2022.nllp-1.18
%P 208-217

Markdown (Informal)

[Efficient Deep Learning-based Sentence Boundary Detection in Legal Text](https://aclanthology.org/2022.nllp-1.18) (Sheik et al., NLLP 2022)

• Efficient Deep Learning-based Sentence Boundary Detection in Legal Text (Sheik et al., NLLP 2022)

ACL

• Reshma Sheik, Gokul T, and S Nirmala. 2022. Efficient Deep Learning-based Sentence Boundary Detection in Legal Text. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 208–217, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.