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        <title>Relating Relations: Meta-Relation Extraction from Online Health Forum Posts</title>
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            <namePart type="given">Ionut-Teodor</namePart>
            <namePart type="family">Sorodoc</namePart>
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        <name type="personal">
            <namePart type="given">Madhumita</namePart>
            <namePart type="family">Sushil</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ece</namePart>
            <namePart type="family">Takmaz</namePart>
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        <name type="personal">
            <namePart type="given">Eneko</namePart>
            <namePart type="family">Agirre</namePart>
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    <abstract>Relation extraction is a key task in knowledge extraction, and is commonly defined as the task of identifying relations that hold between entities in text. This thesis proposal addresses the specific task of identifying meta-relations, a higher order family of relations naturally construed as holding between other relations which includes temporal, comparative, and causal relations. More specifically, we aim to develop theoretical underpinnings and practical solutions for the challenges of (1) incorporating meta-relations into conceptualisations and annotation schemes for (lower-order) relations and named entities, (2) obtaining annotations for them with tolerable cognitive load on annotators, (3) creating models capable of reliably extracting meta-relations, and related to that (4) addressing the limited-data problem exacerbated by the introduction of meta-relations into the learning task. We explore recent works in relation extraction and discuss our plans to formally conceptualise meta-relations for the domain of user-generated health texts, and create a new dataset, annotation scheme and models for meta-relation extraction.</abstract>
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%0 Conference Proceedings
%T Relating Relations: Meta-Relation Extraction from Online Health Forum Posts
%A Stickley, Daniel
%Y Sorodoc, Ionut-Teodor
%Y Sushil, Madhumita
%Y Takmaz, Ece
%Y Agirre, Eneko
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F stickley-2021-relating
%X Relation extraction is a key task in knowledge extraction, and is commonly defined as the task of identifying relations that hold between entities in text. This thesis proposal addresses the specific task of identifying meta-relations, a higher order family of relations naturally construed as holding between other relations which includes temporal, comparative, and causal relations. More specifically, we aim to develop theoretical underpinnings and practical solutions for the challenges of (1) incorporating meta-relations into conceptualisations and annotation schemes for (lower-order) relations and named entities, (2) obtaining annotations for them with tolerable cognitive load on annotators, (3) creating models capable of reliably extracting meta-relations, and related to that (4) addressing the limited-data problem exacerbated by the introduction of meta-relations into the learning task. We explore recent works in relation extraction and discuss our plans to formally conceptualise meta-relations for the domain of user-generated health texts, and create a new dataset, annotation scheme and models for meta-relation extraction.
%R 10.18653/v1/2021.eacl-srw.18
%U https://aclanthology.org/2021.eacl-srw.18
%U https://doi.org/10.18653/v1/2021.eacl-srw.18
%P 129-136

Markdown (Informal)

[Relating Relations: Meta-Relation Extraction from Online Health Forum Posts](https://aclanthology.org/2021.eacl-srw.18) (Stickley, EACL 2021)

• Relating Relations: Meta-Relation Extraction from Online Health Forum Posts (Stickley, EACL 2021)

ACL

• Daniel Stickley. 2021. Relating Relations: Meta-Relation Extraction from Online Health Forum Posts. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 129–136, Online. Association for Computational Linguistics.