Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures

This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-10
Main Authors: Inoue, Shumpei, Minh-Tien Nguyen, Mizokuchi, Hiroki, Nguyen, Tuan-Anh D, Nguyen, Huu-Hiep, Dung Tien Le
Format: Article
Language:English
Subjects:
Datasets
High pressure
Information retrieval
Safety
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Inoue, Shumpei
Minh-Tien Nguyen
Mizokuchi, Hiroki
Nguyen, Tuan-Anh D
Nguyen, Huu-Hiep
Dung Tien Le
description This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2878920118</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2878920118</sourcerecordid><originalsourceid>FETCH-proquest_journals_28789201183</originalsourceid><addsrcrecordid>eNqNikEKwjAQAIMgWNQ_LHgutKna6k3UqicFvUuw25pSEt1Nq883gg_wNAwzPRHIJInDbCrlQIyZ6yiK5DyVs1kSCLrYl6KC4axKJDg-kJTT1vASVga2b-8u1KazTYcFbJRTjA5sCXtd3cMTIXNLCDvFcDA3XaBxDKUl8Knzok0Feeu-T65048kj0S9Vwzj-cSgm-fay3ocPss8W2V1r25Lx6SqzNFvIKI6z5L_rA0y2S10</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2878920118</pqid></control><display><type>article</type><title>Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures</title><source>Publicly Available Content (ProQuest)</source><creator>Inoue, Shumpei ; Minh-Tien Nguyen ; Mizokuchi, Hiroki ; Nguyen, Tuan-Anh D ; Nguyen, Huu-Hiep ; Dung Tien Le</creator><creatorcontrib>Inoue, Shumpei ; Minh-Tien Nguyen ; Mizokuchi, Hiroki ; Nguyen, Tuan-Anh D ; Nguyen, Huu-Hiep ; Dung Tien Le</creatorcontrib><description>This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Datasets ; High pressure ; Information retrieval ; Safety</subject><ispartof>arXiv.org, 2023-10</ispartof><rights>2023. 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>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2878920118?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>777,781,25734,36993,44571</link.rule.ids></links><search><creatorcontrib>Inoue, Shumpei</creatorcontrib><creatorcontrib>Minh-Tien Nguyen</creatorcontrib><creatorcontrib>Mizokuchi, Hiroki</creatorcontrib><creatorcontrib>Nguyen, Tuan-Anh D</creatorcontrib><creatorcontrib>Nguyen, Huu-Hiep</creatorcontrib><creatorcontrib>Dung Tien Le</creatorcontrib><title>Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures</title><title>arXiv.org</title><description>This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).</description><subject>Datasets</subject><subject>High pressure</subject><subject>Information retrieval</subject><subject>Safety</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNikEKwjAQAIMgWNQ_LHgutKna6k3UqicFvUuw25pSEt1Nq883gg_wNAwzPRHIJInDbCrlQIyZ6yiK5DyVs1kSCLrYl6KC4axKJDg-kJTT1vASVga2b-8u1KazTYcFbJRTjA5sCXtd3cMTIXNLCDvFcDA3XaBxDKUl8Knzok0Feeu-T65048kj0S9Vwzj-cSgm-fay3ocPss8W2V1r25Lx6SqzNFvIKI6z5L_rA0y2S10</recordid><startdate>20231023</startdate><enddate>20231023</enddate><creator>Inoue, Shumpei</creator><creator>Minh-Tien Nguyen</creator><creator>Mizokuchi, Hiroki</creator><creator>Nguyen, Tuan-Anh D</creator><creator>Nguyen, Huu-Hiep</creator><creator>Dung Tien Le</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>20231023</creationdate><title>Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures</title><author>Inoue, Shumpei ; Minh-Tien Nguyen ; Mizokuchi, Hiroki ; Nguyen, Tuan-Anh D ; Nguyen, Huu-Hiep ; Dung Tien Le</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_28789201183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Datasets</topic><topic>High pressure</topic><topic>Information retrieval</topic><topic>Safety</topic><toplevel>online_resources</toplevel><creatorcontrib>Inoue, Shumpei</creatorcontrib><creatorcontrib>Minh-Tien Nguyen</creatorcontrib><creatorcontrib>Mizokuchi, Hiroki</creatorcontrib><creatorcontrib>Nguyen, Tuan-Anh D</creatorcontrib><creatorcontrib>Nguyen, Huu-Hiep</creatorcontrib><creatorcontrib>Dung Tien Le</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Engineering Database</collection><collection>Publicly Available Content (ProQuest)</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>Inoue, Shumpei</au><au>Minh-Tien Nguyen</au><au>Mizokuchi, Hiroki</au><au>Nguyen, Tuan-Anh D</au><au>Nguyen, Huu-Hiep</au><au>Dung Tien Le</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures</atitle><jtitle>arXiv.org</jtitle><date>2023-10-23</date><risdate>2023</risdate><eissn>2331-8422</eissn><abstract>This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).</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, 2023-10
issn 2331-8422
language eng
recordid cdi_proquest_journals_2878920118
source Publicly Available Content (ProQuest)
subjects Datasets
High pressure
Information retrieval
Safety
title Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T15%3A54%3A51IST&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=Towards%20Safer%20Operations:%20An%20Expert-involved%20Dataset%20of%20High-Pressure%20Gas%20Incidents%20for%20Preventing%20Future%20Failures&rft.jtitle=arXiv.org&rft.au=Inoue,%20Shumpei&rft.date=2023-10-23&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2878920118%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_28789201183%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2878920118&rft_id=info:pmid/&rfr_iscdi=true