Fine-grained biomedical knowledge negation detection via contrastive learning

Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especi...
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Autor*in:	Zhu, Tiantian [verfasserIn] Xiang, Yang [verfasserIn] Chen, Qingcai [verfasserIn] Qin, Yang [verfasserIn] Hu, Baotian [verfasserIn] Zhang, Wentai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Negation detection Contrastive learning Gating mechanism Datasets

Übergeordnetes Werk:	Enthalten in: Knowledge-based systems - Amsterdam [u.a.] : Elsevier Science, 1987, 272
Übergeordnetes Werk:	volume:272

DOI / URN:	10.1016/j.knosys.2023.110575

Katalog-ID:	ELV009941975

Internformat


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520			\|a Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection.
650		4	\|a Negation detection
650		4	\|a Contrastive learning
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publishDate	2023
allfields	10.1016/j.knosys.2023.110575 doi (DE-627)ELV009941975 (ELSEVIER)S0950-7051(23)00325-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhu, Tiantian verfasserin (orcid)0000-0002-5470-8309 aut Fine-grained biomedical knowledge negation detection via contrastive learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. Negation detection Contrastive learning Gating mechanism Datasets Xiang, Yang verfasserin aut Chen, Qingcai verfasserin aut Qin, Yang verfasserin aut Hu, Baotian verfasserin aut Zhang, Wentai verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 272 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:272 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 272
spelling	10.1016/j.knosys.2023.110575 doi (DE-627)ELV009941975 (ELSEVIER)S0950-7051(23)00325-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhu, Tiantian verfasserin (orcid)0000-0002-5470-8309 aut Fine-grained biomedical knowledge negation detection via contrastive learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. Negation detection Contrastive learning Gating mechanism Datasets Xiang, Yang verfasserin aut Chen, Qingcai verfasserin aut Qin, Yang verfasserin aut Hu, Baotian verfasserin aut Zhang, Wentai verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 272 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:272 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 272
allfields_unstemmed	10.1016/j.knosys.2023.110575 doi (DE-627)ELV009941975 (ELSEVIER)S0950-7051(23)00325-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhu, Tiantian verfasserin (orcid)0000-0002-5470-8309 aut Fine-grained biomedical knowledge negation detection via contrastive learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. Negation detection Contrastive learning Gating mechanism Datasets Xiang, Yang verfasserin aut Chen, Qingcai verfasserin aut Qin, Yang verfasserin aut Hu, Baotian verfasserin aut Zhang, Wentai verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 272 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:272 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 272
allfieldsGer	10.1016/j.knosys.2023.110575 doi (DE-627)ELV009941975 (ELSEVIER)S0950-7051(23)00325-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhu, Tiantian verfasserin (orcid)0000-0002-5470-8309 aut Fine-grained biomedical knowledge negation detection via contrastive learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. Negation detection Contrastive learning Gating mechanism Datasets Xiang, Yang verfasserin aut Chen, Qingcai verfasserin aut Qin, Yang verfasserin aut Hu, Baotian verfasserin aut Zhang, Wentai verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 272 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:272 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 272
allfieldsSound	10.1016/j.knosys.2023.110575 doi (DE-627)ELV009941975 (ELSEVIER)S0950-7051(23)00325-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhu, Tiantian verfasserin (orcid)0000-0002-5470-8309 aut Fine-grained biomedical knowledge negation detection via contrastive learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection. Negation detection Contrastive learning Gating mechanism Datasets Xiang, Yang verfasserin aut Chen, Qingcai verfasserin aut Qin, Yang verfasserin aut Hu, Baotian verfasserin aut Zhang, Wentai verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 272 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:272 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 272
language	English
source	Enthalten in Knowledge-based systems 272 volume:272
sourceStr	Enthalten in Knowledge-based systems 272 volume:272
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Negation detection Contrastive learning Gating mechanism Datasets
dewey-raw	004
isfreeaccess_bool	false
container_title	Knowledge-based systems
authorswithroles_txt_mv	Zhu, Tiantian @@aut@@ Xiang, Yang @@aut@@ Chen, Qingcai @@aut@@ Qin, Yang @@aut@@ Hu, Baotian @@aut@@ Zhang, Wentai @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320580024
dewey-sort	14
id	ELV009941975
language_de	englisch
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author	Zhu, Tiantian
spellingShingle	Zhu, Tiantian ddc 004 bkl 54.72 misc Negation detection misc Contrastive learning misc Gating mechanism misc Datasets Fine-grained biomedical knowledge negation detection via contrastive learning
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title	Fine-grained biomedical knowledge negation detection via contrastive learning
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title_sort	fine-grained biomedical knowledge negation detection via contrastive learning
title_auth	Fine-grained biomedical knowledge negation detection via contrastive learning
abstract	Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection.
abstractGer	Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection.
abstract_unstemmed	Negation is a common and complex phenomenon in natural language, which will reverse the truth value of knowledge extracted from sentences and make knowledge validation indispensable. Negation detection at the granularity of knowledge triples is still a challenging and minimally explored task, especially in the biomedical domain where multiple knowledge triples frequently exist in one sentence and the same triple can express quite distinct meanings under different contexts. In this paper, we illustrate and define the label conflict problem in biomedical negation detection and propose CL-GSAN, a novel Contrastive Learning-based Gated Structured Attention Network for identifying the negated knowledge triples from the biomedical text. CL-GSAN jointly performs fine-grained knowledge-context modeling within a sentence via a gating mechanism and learns the target triple’s context representations by considering the semantic divergence of claims via contrastive learning. Experimental results demonstrate that CL-GSAN obtains promising performance on three biomedical corpora, including the Chinese Medical Knowledge Negation (CMKN) corpus constructed by us to facilitate fine-grained, i.e., triple-level, knowledge negation detection.
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title_short	Fine-grained biomedical knowledge negation detection via contrastive learning
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Fine-grained biomedical knowledge negation detection via contrastive learning

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