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EMIL: Extracting Meaning from Inconsistent Language

Hannes Strass, Adam Wyner Orcid Logo, Martin Diller

International Journal of Approximate Reasoning, Volume: 112, Pages: 55 - 84

Swansea University Author: Adam Wyner Orcid Logo

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DOI (Published version): 10.1016/j.ijar.2019.04.010

Abstract

Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge...

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Published in: International Journal of Approximate Reasoning
ISSN: 0888613X
Published: Elsevier 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa50680
first_indexed 2019-06-06T20:54:03Z
last_indexed 2023-01-11T14:27:11Z
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spelling 2022-09-28T16:44:26.1545813 v2 50680 2019-06-06 EMIL: Extracting Meaning from Inconsistent Language 51fa34a3136b8e81fc273fce73e88099 0000-0002-2958-3428 Adam Wyner Adam Wyner true false 2019-06-06 MACS Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge so as to explain and justify arguments in a humanly accessible form. Yet, there is a gap between the coarse-grained, semi-structured knowledge-bases of computational theories of argumentation and fine-grained, highly-structured inferences from knowledge-bases derived from natural language. We identify several subproblems which must be addressed in order to bridge the gap. We provide a direct semantics for argumentation. It has attractive properties in terms of expressivity and complexity, enables reasoning by cases, and can be more highly structured. For language processing, we work with an existing controlled natural language (CNL), which interfaces with our computational theory of argumentation; the tool processes natural language input, translates them into a form for automated inference engines, outputs argument extensions, then generates natural language statements. The key novel adaptation incorporates the defeasible expression ‘it is usual that’. This is an important, albeit incremental, step to incorporate linguistic expressions of defeasibility. Overall, the novel contribution of the paper is an integrated, end-to-end argumentation system which bridges between automated defeasible reasoning and a natural language interface. Specific novel contributions are the theory of ‘direct semantics’, motivations for our theory, results with respect to the direct semantics, an implementation, experimental results, the tie between the formalisation and the CNL, the introduction into a CNL of a natural language expression of defeasibility, and an ‘engineering’ approach to fine-grained argument analysis. Journal Article International Journal of Approximate Reasoning 112 55 84 Elsevier 0888613X 1 9 2019 2019-09-01 10.1016/j.ijar.2019.04.010 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2022-09-28T16:44:26.1545813 2019-06-06T17:00:08.2210972 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Hannes Strass 1 Adam Wyner 0000-0002-2958-3428 2 Martin Diller 3 0050680-06062019173151.pdf EMIL_Resubmit.pdf 2019-06-06T17:31:51.3100000 Output 390833 application/pdf Accepted Manuscript true 2020-05-13T00:00:00.0000000 Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND). true eng
title EMIL: Extracting Meaning from Inconsistent Language
spellingShingle EMIL: Extracting Meaning from Inconsistent Language
Adam Wyner
title_short EMIL: Extracting Meaning from Inconsistent Language
title_full EMIL: Extracting Meaning from Inconsistent Language
title_fullStr EMIL: Extracting Meaning from Inconsistent Language
title_full_unstemmed EMIL: Extracting Meaning from Inconsistent Language
title_sort EMIL: Extracting Meaning from Inconsistent Language
author_id_str_mv 51fa34a3136b8e81fc273fce73e88099
author_id_fullname_str_mv 51fa34a3136b8e81fc273fce73e88099_***_Adam Wyner
author Adam Wyner
author2 Hannes Strass
Adam Wyner
Martin Diller
format Journal article
container_title International Journal of Approximate Reasoning
container_volume 112
container_start_page 55
publishDate 2019
institution Swansea University
issn 0888613X
doi_str_mv 10.1016/j.ijar.2019.04.010
publisher Elsevier
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Developments in formal and computational theories of argumentation reason with inconsistency. Developments in Computational Linguistics extract arguments from large textual corpora. Both developments head in the direction of automated processing and reasoning with inconsistent, linguistic knowledge so as to explain and justify arguments in a humanly accessible form. Yet, there is a gap between the coarse-grained, semi-structured knowledge-bases of computational theories of argumentation and fine-grained, highly-structured inferences from knowledge-bases derived from natural language. We identify several subproblems which must be addressed in order to bridge the gap. We provide a direct semantics for argumentation. It has attractive properties in terms of expressivity and complexity, enables reasoning by cases, and can be more highly structured. For language processing, we work with an existing controlled natural language (CNL), which interfaces with our computational theory of argumentation; the tool processes natural language input, translates them into a form for automated inference engines, outputs argument extensions, then generates natural language statements. The key novel adaptation incorporates the defeasible expression ‘it is usual that’. This is an important, albeit incremental, step to incorporate linguistic expressions of defeasibility. Overall, the novel contribution of the paper is an integrated, end-to-end argumentation system which bridges between automated defeasible reasoning and a natural language interface. Specific novel contributions are the theory of ‘direct semantics’, motivations for our theory, results with respect to the direct semantics, an implementation, experimental results, the tie between the formalisation and the CNL, the introduction into a CNL of a natural language expression of defeasibility, and an ‘engineering’ approach to fine-grained argument analysis.
published_date 2019-09-01T01:59:14Z
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score 11.04748

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