A relation aware embedding mechanism for relation extraction

Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should con...
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Autor*in:	Li, Xiang [verfasserIn] Li, Yuwei Yang, Junan Liu, Hui Hu, Pengjiang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Relation extraction Information extraction Knowledge graph Attention mechanism

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 52(2022), 9 vom: 11. Jan., Seite 10022-10031
Übergeordnetes Werk:	volume:52 ; year:2022 ; number:9 ; day:11 ; month:01 ; pages:10022-10031

Links:	Volltext

DOI / URN:	10.1007/s10489-021-02699-3

Katalog-ID:	SPR047343958

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520			\|a Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction.
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allfields	10.1007/s10489-021-02699-3 doi (DE-627)SPR047343958 (SPR)s10489-021-02699-3-e DE-627 ger DE-627 rakwb eng Li, Xiang verfasserin aut A relation aware embedding mechanism for relation extraction 2022 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 2021 Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Yuwei aut Yang, Junan (orcid)0000-0002-9336-2152 aut Liu, Hui aut Hu, Pengjiang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 9 vom: 11. Jan., Seite 10022-10031 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031 https://dx.doi.org/10.1007/s10489-021-02699-3 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 52 2022 9 11 01 10022-10031
spelling	10.1007/s10489-021-02699-3 doi (DE-627)SPR047343958 (SPR)s10489-021-02699-3-e DE-627 ger DE-627 rakwb eng Li, Xiang verfasserin aut A relation aware embedding mechanism for relation extraction 2022 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 2021 Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Yuwei aut Yang, Junan (orcid)0000-0002-9336-2152 aut Liu, Hui aut Hu, Pengjiang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 9 vom: 11. Jan., Seite 10022-10031 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031 https://dx.doi.org/10.1007/s10489-021-02699-3 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 52 2022 9 11 01 10022-10031
allfields_unstemmed	10.1007/s10489-021-02699-3 doi (DE-627)SPR047343958 (SPR)s10489-021-02699-3-e DE-627 ger DE-627 rakwb eng Li, Xiang verfasserin aut A relation aware embedding mechanism for relation extraction 2022 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 2021 Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Yuwei aut Yang, Junan (orcid)0000-0002-9336-2152 aut Liu, Hui aut Hu, Pengjiang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 9 vom: 11. Jan., Seite 10022-10031 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031 https://dx.doi.org/10.1007/s10489-021-02699-3 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 52 2022 9 11 01 10022-10031
allfieldsGer	10.1007/s10489-021-02699-3 doi (DE-627)SPR047343958 (SPR)s10489-021-02699-3-e DE-627 ger DE-627 rakwb eng Li, Xiang verfasserin aut A relation aware embedding mechanism for relation extraction 2022 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 2021 Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Yuwei aut Yang, Junan (orcid)0000-0002-9336-2152 aut Liu, Hui aut Hu, Pengjiang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 9 vom: 11. Jan., Seite 10022-10031 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031 https://dx.doi.org/10.1007/s10489-021-02699-3 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 52 2022 9 11 01 10022-10031
allfieldsSound	10.1007/s10489-021-02699-3 doi (DE-627)SPR047343958 (SPR)s10489-021-02699-3-e DE-627 ger DE-627 rakwb eng Li, Xiang verfasserin aut A relation aware embedding mechanism for relation extraction 2022 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 2021 Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213 Li, Yuwei aut Yang, Junan (orcid)0000-0002-9336-2152 aut Liu, Hui aut Hu, Pengjiang aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 9 vom: 11. Jan., Seite 10022-10031 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031 https://dx.doi.org/10.1007/s10489-021-02699-3 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 52 2022 9 11 01 10022-10031
language	English
source	Enthalten in Applied intelligence 52(2022), 9 vom: 11. Jan., Seite 10022-10031 volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031
sourceStr	Enthalten in Applied intelligence 52(2022), 9 vom: 11. Jan., Seite 10022-10031 volume:52 year:2022 number:9 day:11 month:01 pages:10022-10031
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Relation extraction Information extraction Knowledge graph Attention mechanism
isfreeaccess_bool	false
container_title	Applied intelligence
authorswithroles_txt_mv	Li, Xiang @@aut@@ Li, Yuwei @@aut@@ Yang, Junan @@aut@@ Liu, Hui @@aut@@ Hu, Pengjiang @@aut@@
publishDateDaySort_date	2022-01-11T00:00:00Z
hierarchy_top_id	271180919
id	SPR047343958
language_de	englisch
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author	Li, Xiang
spellingShingle	Li, Xiang misc Relation extraction misc Information extraction misc Knowledge graph misc Attention mechanism A relation aware embedding mechanism for relation extraction
authorStr	Li, Xiang
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topic_title	A relation aware embedding mechanism for relation extraction Relation extraction (dpeaa)DE-He213 Information extraction (dpeaa)DE-He213 Knowledge graph (dpeaa)DE-He213 Attention mechanism (dpeaa)DE-He213
topic	misc Relation extraction misc Information extraction misc Knowledge graph misc Attention mechanism
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title	A relation aware embedding mechanism for relation extraction
ctrlnum	(DE-627)SPR047343958 (SPR)s10489-021-02699-3-e
title_full	A relation aware embedding mechanism for relation extraction
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title_sort	relation aware embedding mechanism for relation extraction
title_auth	A relation aware embedding mechanism for relation extraction
abstract	Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	A relation aware embedding mechanism for relation extraction
url	https://dx.doi.org/10.1007/s10489-021-02699-3
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author2	Li, Yuwei Yang, Junan Liu, Hui Hu, Pengjiang
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up_date	2024-07-04T02:48:09.190Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047343958</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507212307.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220621s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-021-02699-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047343958</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-021-02699-3-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">Li, Xiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A relation aware embedding mechanism for relation extraction</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Extracting possible relational triples from natural language text is a fundamental task of information extraction, which has attracted extensive attention. The embedding mechanism has a significant impact on the performance of relation extraction models, and the embedding vectors should contain rich semantic information that has close relevance to the relation extraction task. Driven by this motivation, we propose a RelationAware Embedding Mechanism (RA) for relation extraction. In specific, this mechanism incorporates the relation label information into sentence embedding by leveraging the attention mechanism to distinguish the importance of different relation labels to each word of a sentence. We apply the proposed method to three state-of-the-art relation extraction models: CasRel, SMHSA and ETL-Span, and implement the corresponding models named RA-CasRel, RA-SMHSA and RA-ETL-Span. To evaluate the effectiveness of our method, we conduct extensive experiments on two widely-used open datasets: NYT and WebNLG, and compare RA-CasRel, RA-SMHSA and RA-ETL-Span with 12 state-of-the-art models. The experimental results show that our method can effectively improve the performance of relation extraction. For instance, RA-CasRel reaches 91.7% and 92.4% of F1-score on NYT and WebNLG, respectively, which is the best performance among all the compared models. We have open sourced the code of our proposed method in [1] to facilitate future research in relation extraction.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Relation extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Knowledge graph</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention mechanism</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Yuwei</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Junan</subfield><subfield code="0">(orcid)0000-0002-9336-2152</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Hui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hu, Pengjiang</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">52(2022), 9 vom: 11. 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A relation aware embedding mechanism for relation extraction

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