Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism
This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Lishuang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
Gated polar attention mechanism Dependency representation learning |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:421 ; year:2021 ; day:15 ; month:01 ; pages:210-221 ; extent:12 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2020.09.020 |
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ELV05213508X |
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| 520 | |a This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. | ||
| 520 | |a This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. | ||
| 650 | 7 | |a Gated polar attention mechanism |2 Elsevier | |
| 650 | 7 | |a Biomedical event detection |2 Elsevier | |
| 650 | 7 | |a Dependency representation learning |2 Elsevier | |
| 650 | 7 | |a Triple context representation learning |2 Elsevier | |
| 650 | 7 | |a Dependency weakness |2 Elsevier | |
| 650 | 7 | |a Sparsity diffusion |2 Elsevier | |
| 700 | 1 | |a Zhang, Beibei |4 oth | |
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10.1016/j.neucom.2020.09.020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001210.pica (DE-627)ELV05213508X (ELSEVIER)S0925-2312(20)31425-9 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Lishuang verfasserin aut Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism 2021transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. Gated polar attention mechanism Elsevier Biomedical event detection Elsevier Dependency representation learning Elsevier Triple context representation learning Elsevier Dependency weakness Elsevier Sparsity diffusion Elsevier Zhang, Beibei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 https://doi.org/10.1016/j.neucom.2020.09.020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 421 2021 15 0115 210-221 12 |
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10.1016/j.neucom.2020.09.020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001210.pica (DE-627)ELV05213508X (ELSEVIER)S0925-2312(20)31425-9 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Lishuang verfasserin aut Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism 2021transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. Gated polar attention mechanism Elsevier Biomedical event detection Elsevier Dependency representation learning Elsevier Triple context representation learning Elsevier Dependency weakness Elsevier Sparsity diffusion Elsevier Zhang, Beibei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 https://doi.org/10.1016/j.neucom.2020.09.020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 421 2021 15 0115 210-221 12 |
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10.1016/j.neucom.2020.09.020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001210.pica (DE-627)ELV05213508X (ELSEVIER)S0925-2312(20)31425-9 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Lishuang verfasserin aut Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism 2021transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. Gated polar attention mechanism Elsevier Biomedical event detection Elsevier Dependency representation learning Elsevier Triple context representation learning Elsevier Dependency weakness Elsevier Sparsity diffusion Elsevier Zhang, Beibei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 https://doi.org/10.1016/j.neucom.2020.09.020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 421 2021 15 0115 210-221 12 |
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10.1016/j.neucom.2020.09.020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001210.pica (DE-627)ELV05213508X (ELSEVIER)S0925-2312(20)31425-9 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Lishuang verfasserin aut Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism 2021transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. Gated polar attention mechanism Elsevier Biomedical event detection Elsevier Dependency representation learning Elsevier Triple context representation learning Elsevier Dependency weakness Elsevier Sparsity diffusion Elsevier Zhang, Beibei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 https://doi.org/10.1016/j.neucom.2020.09.020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 421 2021 15 0115 210-221 12 |
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10.1016/j.neucom.2020.09.020 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001210.pica (DE-627)ELV05213508X (ELSEVIER)S0925-2312(20)31425-9 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Lishuang verfasserin aut Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism 2021transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. Gated polar attention mechanism Elsevier Biomedical event detection Elsevier Dependency representation learning Elsevier Triple context representation learning Elsevier Dependency weakness Elsevier Sparsity diffusion Elsevier Zhang, Beibei oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 https://doi.org/10.1016/j.neucom.2020.09.020 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 421 2021 15 0115 210-221 12 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:421 year:2021 day:15 month:01 pages:210-221 extent:12 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. 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Exploiting dependency information to improve biomedical event detection via gated polar attention mechanism |
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This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. |
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This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. |
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This paper tackles the task of biomedical event detection, which includes identifying and categorizing biomedical event triggers. We find that the current biomedical event detection models driven by dependency fail to benefit more distinct improvement from the existing manual dependency embeddings. Here an interpretable hypothesis for the problem above is, that the model using manual dependency embeddings may suffer from low dependency information density (named as dependency weakness) and diffusion of noises from sparse dependency items (called as sparsity diffusion). We argue that dependency representation learning is more effective than the existing manual dependency embeddings, which can reduce dependency weakness and sparsity diffusion. In this work, we first confirm the hypothesis above and then propose to explicitly apply dependency representation learning and triple context representation learning for the biomedical event detection task via gated polar attention mechanism. In specific, we systematically investigate our model under the gated polar attention mechanism. Experimental results demonstrate that our approach outperforms the recent state-of-the-art methods and achieves the best F-score on the biomedical benchmark MLEE dataset. |
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