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        <title>Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies</title>
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        <namePart type="given">Benjamin</namePart>
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    <name type="personal">
        <namePart type="given">Michael</namePart>
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            <namePart type="given">Julia</namePart>
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    <abstract>We propose the use of modal dependency parses (MDPs) aligned with syntactic dependency parse trees as an avenue for the novel task of claim extraction. MDPs provide a document-level structure that links linguistic expression of events to the conceivers responsible for those expressions. By defining the event-conceiver links as claims and using subgraph pattern matching to exploit the complementarity of these modal links and syntactic claim patterns, we outline a method for aggregating and classifying claims, with the potential for supplying a novel perspective on large natural language data sets. Abstracting away from the task of claim extraction, we prototype an interpretable information extraction (IE) paradigm over sentence- and document-level parse structures, framing inference as subgraph matching and learning as subgraph mining. We make our code open-sourced at https://github.com/BBN-E/nlp-graph-pattern-matching-and-mining.</abstract>
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%0 Conference Proceedings
%T Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies
%A Rozonoyer, Benjamin
%A Zajic, David
%A Heintz, Ilana
%A Selvaggio, Michael
%Y Bonn, Julia
%Y Xue, Nianwen
%S Proceedings of the Fourth International Workshop on Designing Meaning Representations
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F rozonoyer-etal-2023-claim
%X We propose the use of modal dependency parses (MDPs) aligned with syntactic dependency parse trees as an avenue for the novel task of claim extraction. MDPs provide a document-level structure that links linguistic expression of events to the conceivers responsible for those expressions. By defining the event-conceiver links as claims and using subgraph pattern matching to exploit the complementarity of these modal links and syntactic claim patterns, we outline a method for aggregating and classifying claims, with the potential for supplying a novel perspective on large natural language data sets. Abstracting away from the task of claim extraction, we prototype an interpretable information extraction (IE) paradigm over sentence- and document-level parse structures, framing inference as subgraph matching and learning as subgraph mining. We make our code open-sourced at https://github.com/BBN-E/nlp-graph-pattern-matching-and-mining.
%U https://aclanthology.org/2023.dmr-1.12/
%P 122-135

Markdown (Informal)

[Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies](https://aclanthology.org/2023.dmr-1.12/) (Rozonoyer et al., DMR 2023)

• Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies (Rozonoyer et al., DMR 2023)

ACL

• Benjamin Rozonoyer, David Zajic, Ilana Heintz, and Michael Selvaggio. 2023. Claim Extraction via Subgraph Matching over Modal and Syntactic Dependencies. In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 122–135, Nancy, France. Association for Computational Linguistics.