@inproceedings{sanchez-2019-sentence,
title = "Sentence Boundary Detection in Legal Text",
author = "Sanchez, George",
editor = "Aletras, Nikolaos and
Ash, Elliott and
Barrett, Leslie and
Chen, Daniel and
Meyers, Adam and
Preotiuc-Pietro, Daniel and
Rosenberg, David and
Stent, Amanda",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2019",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2204",
doi = "10.18653/v1/W19-2204",
pages = "31--38",
abstract = "In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.",
}
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<abstract>In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.</abstract>
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%0 Conference Proceedings
%T Sentence Boundary Detection in Legal Text
%A Sanchez, George
%Y Aletras, Nikolaos
%Y Ash, Elliott
%Y Barrett, Leslie
%Y Chen, Daniel
%Y Meyers, Adam
%Y Preotiuc-Pietro, Daniel
%Y Rosenberg, David
%Y Stent, Amanda
%S Proceedings of the Natural Legal Language Processing Workshop 2019
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F sanchez-2019-sentence
%X In this paper, we examined several algorithms to detect sentence boundaries in legal text. Legal text presents challenges for sentence tokenizers because of the variety of punctuations and syntax of legal text. Out-of-the-box algorithms perform poorly on legal text affecting further analysis of the text. A novel and domain-specific approach is needed to detect sentence boundaries to further analyze legal text. We present the results of our investigation in this paper.
%R 10.18653/v1/W19-2204
%U https://aclanthology.org/W19-2204
%U https://doi.org/10.18653/v1/W19-2204
%P 31-38
Markdown (Informal)
[Sentence Boundary Detection in Legal Text](https://aclanthology.org/W19-2204) (Sanchez, NAACL 2019)
ACL
- George Sanchez. 2019. Sentence Boundary Detection in Legal Text. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 31–38, Minneapolis, Minnesota. Association for Computational Linguistics.