A fine-grained causality extraction model incorporating relative location coding

Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the com...
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Autor*in:	Wan, Weibing [verfasserIn] Chen, Yang Gao, Yongbin Shao, Chen Zhao, Yuming

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Relative location coding Bi-GCN Quintet annotation Fine grained Complex causal extraction

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 22 vom: 02. Sept., Seite 27163-27176
Übergeordnetes Werk:	volume:53 ; year:2023 ; number:22 ; day:02 ; month:09 ; pages:27163-27176

Links:	Volltext

DOI / URN:	10.1007/s10489-023-04970-1

Katalog-ID:	SPR053519957

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520			\|a Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling.
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allfields	10.1007/s10489-023-04970-1 doi (DE-627)SPR053519957 (SPR)s10489-023-04970-1-e DE-627 ger DE-627 rakwb eng Wan, Weibing verfasserin (orcid)0000-0002-7092-9849 aut A fine-grained causality extraction model incorporating relative location coding 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213 Chen, Yang aut Gao, Yongbin aut Shao, Chen aut Zhao, Yuming aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 22 vom: 02. Sept., Seite 27163-27176 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176 https://dx.doi.org/10.1007/s10489-023-04970-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 22 02 09 27163-27176
spelling	10.1007/s10489-023-04970-1 doi (DE-627)SPR053519957 (SPR)s10489-023-04970-1-e DE-627 ger DE-627 rakwb eng Wan, Weibing verfasserin (orcid)0000-0002-7092-9849 aut A fine-grained causality extraction model incorporating relative location coding 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213 Chen, Yang aut Gao, Yongbin aut Shao, Chen aut Zhao, Yuming aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 22 vom: 02. Sept., Seite 27163-27176 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176 https://dx.doi.org/10.1007/s10489-023-04970-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 22 02 09 27163-27176
allfields_unstemmed	10.1007/s10489-023-04970-1 doi (DE-627)SPR053519957 (SPR)s10489-023-04970-1-e DE-627 ger DE-627 rakwb eng Wan, Weibing verfasserin (orcid)0000-0002-7092-9849 aut A fine-grained causality extraction model incorporating relative location coding 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213 Chen, Yang aut Gao, Yongbin aut Shao, Chen aut Zhao, Yuming aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 22 vom: 02. Sept., Seite 27163-27176 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176 https://dx.doi.org/10.1007/s10489-023-04970-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 22 02 09 27163-27176
allfieldsGer	10.1007/s10489-023-04970-1 doi (DE-627)SPR053519957 (SPR)s10489-023-04970-1-e DE-627 ger DE-627 rakwb eng Wan, Weibing verfasserin (orcid)0000-0002-7092-9849 aut A fine-grained causality extraction model incorporating relative location coding 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213 Chen, Yang aut Gao, Yongbin aut Shao, Chen aut Zhao, Yuming aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 22 vom: 02. Sept., Seite 27163-27176 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176 https://dx.doi.org/10.1007/s10489-023-04970-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 22 02 09 27163-27176
allfieldsSound	10.1007/s10489-023-04970-1 doi (DE-627)SPR053519957 (SPR)s10489-023-04970-1-e DE-627 ger DE-627 rakwb eng Wan, Weibing verfasserin (orcid)0000-0002-7092-9849 aut A fine-grained causality extraction model incorporating relative location coding 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213 Chen, Yang aut Gao, Yongbin aut Shao, Chen aut Zhao, Yuming aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 22 vom: 02. Sept., Seite 27163-27176 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176 https://dx.doi.org/10.1007/s10489-023-04970-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 22 02 09 27163-27176
language	English
source	Enthalten in Applied intelligence 53(2023), 22 vom: 02. Sept., Seite 27163-27176 volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176
sourceStr	Enthalten in Applied intelligence 53(2023), 22 vom: 02. Sept., Seite 27163-27176 volume:53 year:2023 number:22 day:02 month:09 pages:27163-27176
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Relative location coding Bi-GCN Quintet annotation Fine grained Complex causal extraction
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Wan, Weibing @@aut@@ Chen, Yang @@aut@@ Gao, Yongbin @@aut@@ Shao, Chen @@aut@@ Zhao, Yuming @@aut@@
publishDateDaySort_date	2023-09-02T00:00:00Z
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language_de	englisch
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author	Wan, Weibing
spellingShingle	Wan, Weibing misc Relative location coding misc Bi-GCN misc Quintet annotation misc Fine grained misc Complex causal extraction A fine-grained causality extraction model incorporating relative location coding
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topic_title	A fine-grained causality extraction model incorporating relative location coding Relative location coding (dpeaa)DE-He213 Bi-GCN (dpeaa)DE-He213 Quintet annotation (dpeaa)DE-He213 Fine grained (dpeaa)DE-He213 Complex causal extraction (dpeaa)DE-He213
topic	misc Relative location coding misc Bi-GCN misc Quintet annotation misc Fine grained misc Complex causal extraction
topic_unstemmed	misc Relative location coding misc Bi-GCN misc Quintet annotation misc Fine grained misc Complex causal extraction
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title	A fine-grained causality extraction model incorporating relative location coding
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title_full	A fine-grained causality extraction model incorporating relative location coding
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author_browse	Wan, Weibing Chen, Yang Gao, Yongbin Shao, Chen Zhao, Yuming
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title_sort	fine-grained causality extraction model incorporating relative location coding
title_auth	A fine-grained causality extraction model incorporating relative location coding
abstract	Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A fine-grained causality extraction model incorporating relative location coding
url	https://dx.doi.org/10.1007/s10489-023-04970-1
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author2	Chen, Yang Gao, Yongbin Shao, Chen Zhao, Yuming
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up_date	2024-07-03T20:07:36.367Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053519957</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231026064724.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231026s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-023-04970-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053519957</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-023-04970-1-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wan, Weibing</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-7092-9849</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A fine-grained causality extraction model incorporating relative location coding</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Popular methods of causality extraction work well for simple and explicit single causal relations, but it remains challenging to extract causal relations from the complex sentences of natural texts due to ambiguity concerning the locations of the causal subject and object as well as the complexity of the relevant dependencies. To solve these problems, this paper proposes a five-tuple annotation scheme that defines a scoring function to iteratively parse out the causal pairs from multiple entity pairs in a sentence, and to thus transform the task of extracting causal relations into that of automatic annotation. First, this study uses this scheme to propose a multi-headed, self-attentive mechanism that incorporates encoded information on relative position to increase the capability of the model to perceive causal features. Second, the authors combine information from a dependency tree while assigning the appropriate weights, and finally use a bidirectional GCN network to parse the weights of features of the tree from multiple perspectives and splice the dependency-related features. This joint model of extraction improves the bounds of cause–effect pairs of entities while considering the dependency relationships between them, which renders the extracted, fine-grained causal terms more accurate. Experiments on the SemEval 2010 task 8 and the ADE datasets show that our approach significantly outperforms prevalent methods in terms of the accuracy of solving complex causal extraction compared with state-of-the-art approaches to modeling.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Relative location coding</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bi-GCN</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quintet annotation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fine grained</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complex causal extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Yongbin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shao, Chen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Yuming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991</subfield><subfield code="g">53(2023), 22 vom: 02. 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