@inproceedings{lamarche-langlais-2024-benchie,
title = "{B}ench{IE}{\textasciicircum}{FL}: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark",
author = "Lamarche, Fabrice and
Langlais, Philippe",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.496",
pages = "8372--8394",
abstract = "Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE{\textasciicircum}FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE{\textasciicircum}FL allows insightful conclusions to be drawn on the actual performance of OIE extractors.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lamarche-langlais-2024-benchie">
<titleInfo>
<title>BenchIE⌃FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark</title>
</titleInfo>
<name type="personal">
<namePart type="given">Fabrice</namePart>
<namePart type="family">Lamarche</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philippe</namePart>
<namePart type="family">Langlais</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics ACL 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Lun-Wei</namePart>
<namePart type="family">Ku</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andre</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vivek</namePart>
<namePart type="family">Srikumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE⌃FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE⌃FL allows insightful conclusions to be drawn on the actual performance of OIE extractors.</abstract>
<identifier type="citekey">lamarche-langlais-2024-benchie</identifier>
<location>
<url>https://aclanthology.org/2024.findings-acl.496</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>8372</start>
<end>8394</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T BenchIE⌃FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark
%A Lamarche, Fabrice
%A Langlais, Philippe
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F lamarche-langlais-2024-benchie
%X Open Information Extraction (OIE) is a field of natural language processing that aims to present textual information in a format that allows it to be organized, analyzed and reflected upon. Numerous OIE systems are developed, claiming ever-increasing performance, marking the need for objective benchmarks. BenchIE is the latest reference we know of. Despite being very well thought out, we noticed a number of issues we believe are limiting. Therefore, we propose BenchIE⌃FL, a new OIE benchmark which fully enforces the principles of BenchIE while containing fewer errors, omissions and shortcomings when candidate facts are matched towards reference ones. BenchIE⌃FL allows insightful conclusions to be drawn on the actual performance of OIE extractors.
%U https://aclanthology.org/2024.findings-acl.496
%P 8372-8394
Markdown (Informal)
[BenchIE^FL: A Manually Re-Annotated Fact-Based Open Information Extraction Benchmark](https://aclanthology.org/2024.findings-acl.496) (Lamarche & Langlais, Findings 2024)
ACL