<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="solawetz-larson-2021-lsoie">
    <titleInfo>
        <title>LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jacob</namePart>
        <namePart type="family">Solawetz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stefan</namePart>
        <namePart type="family">Larson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-apr</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset LSOIE. Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available.</abstract>
    <identifier type="citekey">solawetz-larson-2021-lsoie</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-main.222</identifier>
    <location>
        <url>https://aclanthology.org/2021.eacl-main.222</url>
    </location>
    <part>
        <date>2021-apr</date>
        <extent unit="page">
            <start>2595</start>
            <end>2600</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction
%A Solawetz, Jacob
%A Larson, Stefan
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F solawetz-larson-2021-lsoie
%X Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets are limited in both size and diversity. We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset LSOIE. Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset. We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task. Our LSOIE data, models, and code are made publicly available.
%R 10.18653/v1/2021.eacl-main.222
%U https://aclanthology.org/2021.eacl-main.222
%U https://doi.org/10.18653/v1/2021.eacl-main.222
%P 2595-2600

Markdown (Informal)

[LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction](https://aclanthology.org/2021.eacl-main.222) (Solawetz & Larson, EACL 2021)

• LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction (Solawetz & Larson, EACL 2021)

ACL

• Jacob Solawetz and Stefan Larson. 2021. LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2595–2600, Online. Association for Computational Linguistics.