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        <title>Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
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    <name type="personal">
        <namePart type="given">Elizabeth</namePart>
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            <title>Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop</title>
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        <name type="personal">
            <namePart type="given">Claire</namePart>
            <namePart type="family">Bonial</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nianwen</namePart>
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    <abstract>Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.</abstract>
    <identifier type="citekey">berger-goldstein-2021-increasing</identifier>
    <identifier type="doi">10.18653/v1/2021.law-1.15</identifier>
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%0 Conference Proceedings
%T Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions
%A Berger, Maria
%A Goldstein, Elizabeth
%Y Bonial, Claire
%Y Xue, Nianwen
%S Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F berger-goldstein-2021-increasing
%X Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.
%R 10.18653/v1/2021.law-1.15
%U https://aclanthology.org/2021.law-1.15
%U https://doi.org/10.18653/v1/2021.law-1.15
%P 139-150

Markdown (Informal)

[Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions](https://aclanthology.org/2021.law-1.15) (Berger & Goldstein, LAW 2021)

• Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions (Berger & Goldstein, LAW 2021)

ACL

• Maria Berger and Elizabeth Goldstein. 2021. Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions. In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 139–150, Punta Cana, Dominican Republic. Association for Computational Linguistics.