Legal Fact Prediction: Task Definition and Dataset Construction

Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts ca...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2024-09
Hauptverfasser: Liu, Junkai, Tong, Yujie, Huang, Hui, Zheng, Shuyuan, Yang, Muyun, Wu, Peicheng, Onizuka, Makoto, Xiao, Chuan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Liu, Junkai
Tong, Yujie
Huang, Hui
Zheng, Shuyuan
Yang, Muyun
Wu, Peicheng
Onizuka, Makoto
Xiao, Chuan
description Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.
format Article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3103645548</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3103645548</sourcerecordid><originalsourceid>FETCH-proquest_journals_31036455483</originalsourceid><addsrcrecordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSw90lNT8xRcEtMLlEIKEpNyUwuyczPs1IISSzOVnBJTcvMywQJKCTmpSi4JJYkFqeWKDjn5xWXFJWCVfIwsKYl5hSn8kJpbgZlN9cQZw_dgqL8wtLU4pL4rPzSojygVLyxoYGxmYmpqYmFMXGqALD5OEA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3103645548</pqid></control><display><type>article</type><title>Legal Fact Prediction: Task Definition and Dataset Construction</title><source>Free E- Journals</source><creator>Liu, Junkai ; Tong, Yujie ; Huang, Hui ; Zheng, Shuyuan ; Yang, Muyun ; Wu, Peicheng ; Onizuka, Makoto ; Xiao, Chuan</creator><creatorcontrib>Liu, Junkai ; Tong, Yujie ; Huang, Hui ; Zheng, Shuyuan ; Yang, Muyun ; Wu, Peicheng ; Onizuka, Makoto ; Xiao, Chuan</creatorcontrib><description>Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Datasets ; Predictions</subject><ispartof>arXiv.org, 2024-09</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Liu, Junkai</creatorcontrib><creatorcontrib>Tong, Yujie</creatorcontrib><creatorcontrib>Huang, Hui</creatorcontrib><creatorcontrib>Zheng, Shuyuan</creatorcontrib><creatorcontrib>Yang, Muyun</creatorcontrib><creatorcontrib>Wu, Peicheng</creatorcontrib><creatorcontrib>Onizuka, Makoto</creatorcontrib><creatorcontrib>Xiao, Chuan</creatorcontrib><title>Legal Fact Prediction: Task Definition and Dataset Construction</title><title>arXiv.org</title><description>Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.</description><subject>Datasets</subject><subject>Predictions</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpjYuA0MjY21LUwMTLiYOAtLs4yMDAwMjM3MjU15mSw90lNT8xRcEtMLlEIKEpNyUwuyczPs1IISSzOVnBJTcvMywQJKCTmpSi4JJYkFqeWKDjn5xWXFJWCVfIwsKYl5hSn8kJpbgZlN9cQZw_dgqL8wtLU4pL4rPzSojygVLyxoYGxmYmpqYmFMXGqALD5OEA</recordid><startdate>20240911</startdate><enddate>20240911</enddate><creator>Liu, Junkai</creator><creator>Tong, Yujie</creator><creator>Huang, Hui</creator><creator>Zheng, Shuyuan</creator><creator>Yang, Muyun</creator><creator>Wu, Peicheng</creator><creator>Onizuka, Makoto</creator><creator>Xiao, Chuan</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240911</creationdate><title>Legal Fact Prediction: Task Definition and Dataset Construction</title><author>Liu, Junkai ; Tong, Yujie ; Huang, Hui ; Zheng, Shuyuan ; Yang, Muyun ; Wu, Peicheng ; Onizuka, Makoto ; Xiao, Chuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31036455483</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Datasets</topic><topic>Predictions</topic><toplevel>online_resources</toplevel><creatorcontrib>Liu, Junkai</creatorcontrib><creatorcontrib>Tong, Yujie</creatorcontrib><creatorcontrib>Huang, Hui</creatorcontrib><creatorcontrib>Zheng, Shuyuan</creatorcontrib><creatorcontrib>Yang, Muyun</creatorcontrib><creatorcontrib>Wu, Peicheng</creatorcontrib><creatorcontrib>Onizuka, Makoto</creatorcontrib><creatorcontrib>Xiao, Chuan</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Junkai</au><au>Tong, Yujie</au><au>Huang, Hui</au><au>Zheng, Shuyuan</au><au>Yang, Muyun</au><au>Wu, Peicheng</au><au>Onizuka, Makoto</au><au>Xiao, Chuan</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Legal Fact Prediction: Task Definition and Dataset Construction</atitle><jtitle>arXiv.org</jtitle><date>2024-09-11</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Legal facts refer to the facts that can be proven by acknowledged evidence in a trial. They form the basis for the determination of court judgments. This paper introduces a novel NLP task: legal fact prediction, which aims to predict the legal fact based on a list of evidence. The predicted facts can instruct the parties and their lawyers involved in a trial to strengthen their submissions and optimize their strategies during the trial. Moreover, since real legal facts are difficult to obtain before the final judgment, the predicted facts also serve as an important basis for legal judgment prediction. We construct a benchmark dataset consisting of evidence lists and ground-truth legal facts for real civil loan cases, LFPLoan. Our experiments on this dataset show that this task is non-trivial and requires further considerable research efforts.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2024-09
issn 2331-8422
language eng
recordid cdi_proquest_journals_3103645548
source Free E- Journals
subjects Datasets
Predictions
title Legal Fact Prediction: Task Definition and Dataset Construction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T19%3A50%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Legal%20Fact%20Prediction:%20Task%20Definition%20and%20Dataset%20Construction&rft.jtitle=arXiv.org&rft.au=Liu,%20Junkai&rft.date=2024-09-11&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3103645548%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3103645548&rft_id=info:pmid/&rfr_iscdi=true