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    <titleInfo>
        <title>Open Information Extraction on Scientific Text: An Evaluation</title>
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    <name type="personal">
        <namePart type="given">Paul</namePart>
        <namePart type="family">Groth</namePart>
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        <namePart type="given">Mike</namePart>
        <namePart type="family">Lauruhn</namePart>
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    <name type="personal">
        <namePart type="given">Antony</namePart>
        <namePart type="family">Scerri</namePart>
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    <name type="personal">
        <namePart type="given">Ron</namePart>
        <namePart type="family">Daniel Jr.</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
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            <namePart type="given">Pierre</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.</abstract>
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        <url>https://aclanthology.org/C18-1289</url>
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    <part>
        <date>2018-08</date>
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            <start>3414</start>
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%0 Conference Proceedings
%T Open Information Extraction on Scientific Text: An Evaluation
%A Groth, Paul
%A Lauruhn, Mike
%A Scerri, Antony
%A Daniel Jr., Ron
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F groth-etal-2018-open
%X Open Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks including knowledge base construction, relation extraction, and question answering. While OIE methods are targeted at being domain independent, they have been evaluated primarily on newspaper, encyclopedic or general web text. In this article, we evaluate the performance of OIE on scientific texts originating from 10 different disciplines. To do so, we use two state-of-the-art OIE systems using a crowd-sourcing approach. We find that OIE systems perform significantly worse on scientific text than encyclopedic text. We also provide an error analysis and suggest areas of work to reduce errors. Our corpus of sentences and judgments are made available.
%U https://aclanthology.org/C18-1289
%P 3414-3423

Markdown (Informal)

[Open Information Extraction on Scientific Text: An Evaluation](https://aclanthology.org/C18-1289) (Groth et al., COLING 2018)

• Open Information Extraction on Scientific Text: An Evaluation (Groth et al., COLING 2018)

ACL

• Paul Groth, Mike Lauruhn, Antony Scerri, and Ron Daniel Jr.. 2018. Open Information Extraction on Scientific Text: An Evaluation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3414–3423, Santa Fe, New Mexico, USA. Association for Computational Linguistics.