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| "title": "Review of Entity Relation Extraction based on deep learning", | |
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| "abstract": "As a core task of Information Extraction, Entity Relation Extraction plays an important role in many Natural Language Processing applications such as knowledge graph, intelligent question answering system and semantic search. Relation extraction tasks aim to find the semantic relation between a pair of entity mentions from unstructured texts. This paper focuses on the sentence-level relation extraction, introduces the main datasets for this task, and expounds the current status of relation extraction technology which can be divided into: supervised relation extraction, distant supervision relation extraction and joint extraction of entities and relations. We compare the various models for this task and analyze their contributions and defects. Finally, the research status and methods of Chinese entity relation extraction are introduced.", | |
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| "text": "As a core task of Information Extraction, Entity Relation Extraction plays an important role in many Natural Language Processing applications such as knowledge graph, intelligent question answering system and semantic search. Relation extraction tasks aim to find the semantic relation between a pair of entity mentions from unstructured texts. This paper focuses on the sentence-level relation extraction, introduces the main datasets for this task, and expounds the current status of relation extraction technology which can be divided into: supervised relation extraction, distant supervision relation extraction and joint extraction of entities and relations. We compare the various models for this task and analyze their contributions and defects. Finally, the research status and methods of Chinese entity relation extraction are introduced.", | |
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| "text": "\u662f\u6700\u65e9\u7684\u5229\u7528\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u5173\u7cfb\u62bd\u53d6\u7684\u65b9\u6cd5,\u8be5\u65b9\u6cd5\u6cbf\u7740\u6210 \u5206\u89e3\u6790\u6811\u5efa\u7acb\u9012\u5f52\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60\u6811\u7ed3\u6784\u4e2d\u5355\u8bcd\u6216\u77ed\u8bed\u7684\u8bed\u4e49\u5411\u91cf\u8868\u793a\u3002\u6811\u4e2d\u6bcf\u4e2a\u8282\u70b9\u7531\u77e9\u9635-\u5411 \u91cf(MV)\u8868\u793a\uff0c\u5411\u91cf\u7528\u6765\u8868\u793a\u6210\u5206\u7684\u5185\u5728\u542b\u4e49\uff0c\u77e9\u9635\u7528\u6765\u6355\u83b7\u5b83\u5982\u4f55\u6539\u53d8\u76f8\u90bb\u5355\u8bcd\u6216\u77ed\u8bed\u7684\u542b \u4e49\u3002\u5bf9\u4e8e\u5173\u7cfb\u5206\u7c7b\u4efb\u52a1\uff0c\u8be5\u65b9\u6cd5\u9996\u5148\u5728\u89e3\u6790\u6811\u4e2d\u627e\u5230\u8981\u5bf9\u5176\u5173\u7cfb\u8fdb\u884c\u5206\u7c7b\u7684\u4e24\u4e2a\u5b9e\u4f53\u4e4b\u95f4\u7684\u8def \u5f84\u3002\u7136\u540e\u9009\u62e9\u8def\u5f84\u7684\u6700\u9ad8\u8282\u70b9\uff0c\u5e76\u4f7f\u7528\u8be5\u8282\u70b9\u7684\u5411\u91cf\u4f5c\u4e3a\u7279\u5f81\u5bf9\u5173\u7cfb\u8fdb\u884c\u5206\u7c7b\uff0c\u540c\u65f6\u4ed6\u4eec\u5728\u6a21 \u578b\u4e2d\u52a0\u5165\u8bcd\u6027\u7279\u5f81\u3001\u547d\u540d\u5b9e\u4f53\u6807\u7b7e\u7279\u5f81\u548cWordNet\u8bed\u4e49\u7c7b\u522b\u7279\u5f81\u3002\u8be5\u65b9\u6cd5\u5728SemEval-2010\u6570\u636e \u96c6\u4e0aF1\u503c\u8fbe\u523082.4%\u3002", | |
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| { | |
| "text": "MV-RNN\u5305\u542b\u7279\u5f81\u5b66\u4e60\u8fc7\u7a0b\uff0c\u8fd9\u79cd\u65b9\u6cd5\u4f9d\u8d56\u9012\u5f52\u8fc7\u7a0b\u4e2d\u4f7f\u7528\u7684\u8bed\u6cd5\u6811\uff0c\u53e5\u6cd5\u5206\u6790\u4e2d\u7684\u9519\u8bef \u4f1a\u6291\u5236\u5b66\u4e60\u9ad8\u8d28\u91cf\u7279\u5f81\u7684\u80fd\u529b\u3002\u56e0\u6b64\uff0c\u73b0\u6709\u65b9\u6cd5\u5229\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc(CNN) (Zeng et al., 2014; Kim, 2014; Collobert et al., 2011) (Marcheggiani et al., 2017; Kipf et al., 2016 )\u7684\u542f\u53d1\uff0c\u4e00\u79cd\u9002\u7528\u4e8e\u5173\u7cfb\u62bd\u53d6\u7684\u56fe \u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u7684\u6269\u5c55\u65b9\u6cd5 \u5229\u7528\u9ad8\u6548\u7684\u56fe\u5377\u79ef\u8fd0\u7b97\u5bf9\u8f93\u5165\u8bed\u53e5\u7684\u4f9d\u5b58\u7ed3\u6784\u8fdb \u884c\u7f16\u7801\uff0c\u7136\u540e\u63d0\u53d6\u4ee5\u5b9e\u4f53\u4e3a\u4e2d\u5fc3\u7684\u8868\u793a\uff0c\u5b9e\u73b0\u5173\u7cfb\u9884\u6d4b\u3002\u6b64\u5916\u57fa\u4e8e\u6ce8\u610f\u529b\u5f15\u5bfc\u7684\u56fe\u5377\u79ef\u7f51\u7edc\u65b9 \u6cd5(AGGCNs) (Guo et al., 2019) (Zeng et al., 2015) 72.3 69.7 64.1 -PCNN+MIL (Zeng et al., 2015) 86.0 80.0 -69.0 PCNN+ATT (Lin et al., 2016) 76.2 73.1 67.4 -MIMLCNN (Jiang et al., 2016) 69.0 64.0 59.0 53.0 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u5377 \u5377 \u5377\u79ef \u79ef \u79ef\u795e \u795e \u795e\u7ecf \u7ecf \u7ecf\u7f51 \u7f51 \u7f51\u7edc \u7edc \u7edc \u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 ResCNN-9 (Huang et al., 2017) 79.0 69.0 61.0 -APCNN+D (Ji et al., 2017) 87.0 83.0 -74.0 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u6ce8 \u6ce8 \u6ce8\u610f \u610f \u610f\u529b \u529b \u529b\u673a \u673a \u673a\u5236 \u5236 \u5236\u7684 \u7684 \u7684 \u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 MLSSA (Du et al., 2018) 90.0 81.5 77.0 -LFDS 90.0 88.0 -83.0 \u878d \u878d \u878d\u5408 \u5408 \u5408\u77e5 \u77e5 \u77e5\u8bc6 \u8bc6 \u8bc6\u5e93 \u5e93 \u5e93\u7684 \u7684 \u7684 \u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 RESIDE (Vashishth et al., 2018) 84.0 78.5 75.6 - (Li et al., 2014) 0.835 0.762 0.797 0.608 0.361 0.453 (Miwa et al., 2016) 0.808 0.829 0.818 0.487 0.481 0.484 (Katiyar et al., 2017) 0.812 0.781 0.796 0.502 0.488 0.493 ACE04 0.844 0.829 0.836 0.501 0.487 0.494 (Li et al., 2014) 0.852 0.769 0.808 0.654 0.398 0.495 (Miwa et al., 2016) 0.852 0.769 0.808 0.572 0.540 0.556 (Zhang et al., 2017) -0.836 -0.575 (Katiyar et al., 2017) 0.840 0.813 0.831 0.605 0.553 0.578 (Sun et al., 2018) 0 (Zheng et al., 2017) 0.59 0.479 0.529 0.597 0.451 0.514 (Sun et al., 2018) 0.652 0.406 0.500 (Zeng et al., 2018) 0.610 0.566 0.587 (Takanobu et al., 2019) 0.714 0.586 0.644 (\u6b66\u6587\u96c5 et al., 2019) 78.41% COAE2016 (\u674e\u536b\u7586 et al., 2019) 81.49% (\u6b66\u6587\u96c5 et al., 2019) 73.94% ACE2005 78.71% DuIE 84.8% Table 4 : \u5173\u7cfb\u62bd\u53d6\u5728\u4e2d\u6587\u6570\u636e\u96c6\u4e0a\u7684\u7ed3\u679c 7 \u603b \u603b \u603b\u7ed3 \u7ed3 \u7ed3\u4e0e \u4e0e \u4e0e\u5c55 \u5c55 \u5c55\u671b \u671b \u671b", | |
| "cite_spans": [ | |
| { | |
| "start": 77, | |
| "end": 96, | |
| "text": "(Zeng et al., 2014;", | |
| "ref_id": "BIBREF56" | |
| }, | |
| { | |
| "start": 97, | |
| "end": 107, | |
| "text": "Kim, 2014;", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 108, | |
| "end": 131, | |
| "text": "Collobert et al., 2011)", | |
| "ref_id": "BIBREF3" | |
| }, | |
| { | |
| "start": 132, | |
| "end": 159, | |
| "text": "(Marcheggiani et al., 2017;", | |
| "ref_id": "BIBREF29" | |
| }, | |
| { | |
| "start": 160, | |
| "end": 177, | |
| "text": "Kipf et al., 2016", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 281, | |
| "end": 299, | |
| "text": "(Guo et al., 2019)", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 300, | |
| "end": 319, | |
| "text": "(Zeng et al., 2015)", | |
| "ref_id": "BIBREF55" | |
| }, | |
| { | |
| "start": 345, | |
| "end": 364, | |
| "text": "(Zeng et al., 2015)", | |
| "ref_id": "BIBREF55" | |
| }, | |
| { | |
| "start": 390, | |
| "end": 408, | |
| "text": "(Lin et al., 2016)", | |
| "ref_id": "BIBREF25" | |
| }, | |
| { | |
| "start": 433, | |
| "end": 453, | |
| "text": "(Jiang et al., 2016)", | |
| "ref_id": "BIBREF15" | |
| }, | |
| { | |
| "start": 535, | |
| "end": 555, | |
| "text": "(Huang et al., 2017)", | |
| "ref_id": "BIBREF13" | |
| }, | |
| { | |
| "start": 580, | |
| "end": 597, | |
| "text": "(Ji et al., 2017)", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 672, | |
| "end": 689, | |
| "text": "(Du et al., 2018)", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 776, | |
| "end": 800, | |
| "text": "(Vashishth et al., 2018)", | |
| "ref_id": "BIBREF45" | |
| }, | |
| { | |
| "start": 818, | |
| "end": 835, | |
| "text": "(Li et al., 2014)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 872, | |
| "end": 891, | |
| "text": "(Miwa et al., 2016)", | |
| "ref_id": "BIBREF33" | |
| }, | |
| { | |
| "start": 928, | |
| "end": 950, | |
| "text": "(Katiyar et al., 2017)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 1029, | |
| "end": 1046, | |
| "text": "(Li et al., 2014)", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 1083, | |
| "end": 1102, | |
| "text": "(Miwa et al., 2016)", | |
| "ref_id": "BIBREF33" | |
| }, | |
| { | |
| "start": 1139, | |
| "end": 1159, | |
| "text": "(Zhang et al., 2017)", | |
| "ref_id": "BIBREF58" | |
| }, | |
| { | |
| "start": 1174, | |
| "end": 1196, | |
| "text": "(Katiyar et al., 2017)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 1233, | |
| "end": 1251, | |
| "text": "(Sun et al., 2018)", | |
| "ref_id": "BIBREF42" | |
| }, | |
| { | |
| "start": 1254, | |
| "end": 1274, | |
| "text": "(Zheng et al., 2017)", | |
| "ref_id": "BIBREF61" | |
| }, | |
| { | |
| "start": 1310, | |
| "end": 1328, | |
| "text": "(Sun et al., 2018)", | |
| "ref_id": "BIBREF42" | |
| }, | |
| { | |
| "start": 1347, | |
| "end": 1366, | |
| "text": "(Zeng et al., 2018)", | |
| "ref_id": "BIBREF57" | |
| }, | |
| { | |
| "start": 1385, | |
| "end": 1408, | |
| "text": "(Takanobu et al., 2019)", | |
| "ref_id": "BIBREF43" | |
| }, | |
| { | |
| "start": 1427, | |
| "end": 1445, | |
| "text": "(\u6b66\u6587\u96c5 et al., 2019)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1462, | |
| "end": 1480, | |
| "text": "(\u674e\u536b\u7586 et al., 2019)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1488, | |
| "end": 1506, | |
| "text": "(\u6b66\u6587\u96c5 et al., 2019)", | |
| "ref_id": null | |
| } | |
| ], | |
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| { | |
| "start": 1540, | |
| "end": 1547, | |
| "text": "Table 4", | |
| "ref_id": null | |
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| "text": "\u63d0\u53d6\u8bcd\u6c47\u548c\u53e5\u5b50\u7ea7\u5c42\u6b21\u7279\u5f81\uff0c\u7528\u4e8e\u5173\u7cfb\u62bd\u53d6\u4efb\u52a1\u3002\u8be5\u65b9\u6cd5\u4e0d \u9700\u8981\u590d\u6742\u7684\u53e5\u6cd5\u6216\u8bed\u4e49\u9884\u5904\u7406\uff0c\u6a21\u578b\u7684\u8f93\u5165\u662f\u4e00\u4e2a\u5e26\u6709\u4e24\u4e2a\u6807\u8bb0\u5b9e\u4f53\u540d\u8bcd\u7684\u53e5\u5b50\u3002\u7136\u540e\u901a\u8fc7\u8bcd \u5d4c\u5165\u5c06\u5355\u8bcd\u6620\u5c04\u4e3a\u5206\u5e03\u5f0f\u8bcd\u5411\u91cf\uff0c\u5206\u522b\u63d0\u53d6\u8bcd\u6c47\u7ea7\u7279\u5f81\u548c\u53e5\u5b50\u7ea7\u7279\u5f81\uff0c\u5e76\u76f4\u63a5\u5c06\u4e24\u79cd\u7279\u5f81\u62fc\u63a5 \u5f62\u6210\u6700\u7ec8\u7684\u7279\u5f81\u5411\u91cf\u3002\u5176\u4e2d\uff0c\u5c06\u5e26\u6807\u8bb0\u5b9e\u4f53\u53ca\u5176\u4e0a\u4e0b\u6587\u8bcd\u8bed\u6240\u5bf9\u5e94\u7684\u8bcd\u5411\u91cf\u548cWordNet\u4e2d\u8bed\u4e49 \u7c7b\u522b\u7279\u5f81\u62fc\u63a5\u4f5c\u4e3a\u8bcd\u6c47\u7ea7\u7279\u5f81\u5411\u91cf,\u7528\u6700\u5927\u6c60\u5316\u5377\u79ef\u795e\u7ecf\u7f51\u7edc(CNN)\u81ea\u52a8\u63d0\u53d6\u53e5\u5b50\u7ea7\u7279\u5f81\u8868\u793a\u3002 \u66f4\u8fdb\u4e00\u6b65\uff0cCR-CNN\u6a21\u578b (Santos et al., 2015)\u5229\u7528\u65b0\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5904\u7406\u5173\u7cfb\u5206\u7c7b\u4efb\u52a1,\u5bf9\u4e8e \u7ed9\u5b9a\u7684\u8f93\u5165\u6587\u672c\uff0c\u6a21\u578b\u5c06\u5176\u8f6c\u6362\u6210\u5206\u5e03\u5f0f\u8868\u793a\uff0c\u7136\u540e\u901a\u8fc7\u5377\u79ef\u5c42\u6784\u9020\u53e5\u5b50\u7684\u7279\u5f81\u8868\u793ar\uff0c\u4e0d\u540c \u4e8eZeng\u4f7f\u7528softmax\u8ba1\u7b97\u5f97\u5206\uff0c\u8be5\u65b9\u6cd5\u6a21\u578b\u901a\u8fc7\u7c7b\u522b\u6743\u91cd\u77e9\u9635\u548cr\u6267\u884c\u70b9\u79ef\u64cd\u4f5c\u5f97\u5230\u6bcf\u4e2a\u5173\u7cfb\u7c7b \u522b\u7684\u5f97\u5206\uff0c\u5e76\u4e14\u91c7\u7528\u6392\u540d\u635f\u5931\u51fd\u6570\u8fdb\u884c\u8bad\u7ec3\u3002\u8be5\u5de5\u4f5c\u53ea\u4f7f\u7528\u8bcd\u5411\u91cf\u4f5c\u4e3a\u7279\u5f81\u8f93\u5165\uff0c\u6ca1\u6709\u4f7f\u7528\u4efb \u4f55\u5176\u4ed6\u5916\u90e8\u8d44\u6e90\u3002 3.1.2 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u6700 \u6700 \u6700\u77ed \u77ed \u77ed\u4f9d \u4f9d \u4f9d\u5b58 \u5b58 \u5b58\u8def \u8def \u8def\u5f84 \u5f84 \u5f84\u7684 \u7684 \u7684\u795e \u795e \u795e\u7ecf \u7ecf \u7ecf\u7f51 \u7f51 \u7f51\u7edc \u7edc \u7edc\u6a21 \u6a21 \u6a21\u578b \u578b \u578b \u4e0a\u8ff0\u65b9\u6cd5\u5728\u5173\u7cfb\u62bd\u53d6\u4efb\u52a1\u4e0a\u662f\u6709\u6548\u7684\uff0c\u4f46\u662f\uff0c\u5f53\u4e3b\u8bed\u548c\u5bbe\u8bed\u4e4b\u95f4\u7684\u8ddd\u79bb\u8f83\u957f\u65f6\uff0c\u5f80\u5f80\u4f1a\u53d7 \u5230\u5176\u4ed6\u4e0d\u76f8\u5173\u4fe1\u606f\u7684\u5e72\u6270\u3002\u8003\u8651\u5230\u53e5\u6cd5\u7279\u5f81\u5728\u5173\u7cfb\u8bc6\u522b\u4e2d\u8d77\u5230\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u8bb8\u591a\u7814\u7a76\u901a \u8fc7\u795e\u7ecf\u7f51\u7edc\u4ece\u6700\u77ed\u4f9d\u5b58\u8def\u5f84\u4e2d\u5b66\u4e60\u5173\u7cfb\u8868\u793a\u3002\u8be5\u65b9\u6cd5\u57fa\u4e8e\u6700\u77ed\u8def\u5f84\u5047\u8bbe\uff1a\u5982\u679ce1\u548ce2\u662f\u53e5\u5b50\u4e2d \u7684\u4e24\u4e2a\u540d\u8bcd\u5b9e\u4f53\uff0c\u6211\u4eec\u5047\u8bbee1\u548ce2\u4e4b\u95f4\u7684\u6700\u77ed\u8def\u5f84\uff0c\u63cf\u8ff0\u4e86\u5b83\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u8fd9\u662f\u56e0\u4e3a,(1)\u5982 \u679ce1\u548ce2\u662f\u5c5e\u4e8e\u540c\u4e00\u4e2a\u8c13\u8bcd\u7684\u8bba\u5143\uff0c\u90a3\u4e48\u5b83\u4eec\u7684\u6700\u77ed\u8def\u5f84\u5e94\u8be5\u901a\u8fc7\u8be5\u8c13\u8bcd\uff1b(2)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "EQUATION", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [ | |
| { | |
| "start": 0, | |
| "end": 8, | |
| "text": "EQUATION", | |
| "ref_id": "EQREF", | |
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| "eq_num": "(5)" | |
| } | |
| ], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
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| { | |
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| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Wei", | |
| "middle": [], | |
| "last": "Shi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jun", | |
| "middle": [], | |
| "last": "Tian", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zhenyu", | |
| "middle": [], | |
| "last": "Qi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Bingchen", | |
| "middle": [], | |
| "last": "Li", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hongwei", | |
| "middle": [], | |
| "last": "Hao", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Bo", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2016, | |
| "venue": "The 54th Annual Meeting of the Association for Computational Linguistics", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "207--212", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao and Bo Xu. 2016. Attention-based bidirectional long short-term memory networks for relation classification. The 54th Annual Meeting of the Association for Computational Linguistics, pages 207-212.", | |
| "links": null | |
| } | |
| }, | |
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| "content": "<table><tr><td>\u4f53\uff0crelation\u8868\u793a\u5b9e\u4f53\u4e4b\u95f4\u7684\u8bed\u4e49\u5173\u7cfb\u3002\u4f8b\u5982\uff0c\u7ed9\u51fa\u5982\u4e0b\u7684\u53e5\u5b50\uff1a\u5218\u7fd4\uff0c1983\u5e747\u670813\u65e5\u51fa\u751f\u4e8e</td></tr><tr><td>\u4e0a\u6d77\uff0c\u4e2d\u56fd\u7537\u5b50\u7530\u5f84\u961f110\u7c73\u680f\u8fd0\u884c\u5458\u3002\u6211\u4eec\u53ef\u4ee5\u62bd\u53d6\u51fa\u5b9e\u4f53\u5173\u7cfb\u4e09\u5143\u7ec4\u5b9e\u4f8b(\u5218\u7fd4\uff0c\u51fa\u751f\u5730\uff0c</td></tr><tr><td>\u4e0a\u6d77)\u3002</td></tr><tr><td>\u8fd9\u91cc\u6211\u4eec\u4e3b\u8981\u7814\u7a76\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\uff0c\u5305\u62ec\uff1a\u6709\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u3001\u8fdc\u7a0b\u76d1\u7763\u5173</td></tr><tr><td>\u7cfb\u62bd\u53d6\u548c\u5b9e\u4f53\u5173\u7cfb\u7684\u8054\u5408\u62bd\u53d6\u3002\u57fa\u4e8e\u4f20\u7edf\u65b9\u6cd5\u7684\u5173\u7cfb\u62bd\u53d6\u7814\u7a76\u7efc\u8ff0\u53ef\u53c2\u8003\u6587\u732e (Pawar et al.,</td></tr><tr><td>2017)\u3002\u6b64\u5916\u6211\u4eec\u8fd8\u63a2\u8ba8\u4e2d\u6587\u5173\u7cfb\u62bd\u53d6\u7684\u7814\u7a76\u73b0\u72b6\u3001\u65b9\u6cd5\u7b49\u3002</td></tr><tr><td>\u672c\u6587\u7684\u7ed3\u6784\u5982\u4e0b\uff1a\u7b2c1\u8282\u4ecb\u7ecd\u7528\u4e8e\u5173\u7cfb\u62bd\u53d6\u7684\u6570\u636e\u96c6\uff1b\u7b2c2,3,4\u8282\u5206\u522b\u63a2\u8ba8\u6709\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u3001</td></tr><tr><td>\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u548c\u5b9e\u4f53\u5173\u7cfb\u8054\u5408\u62bd\u53d6\u76f8\u5173\u65b9\u6cd5\u7814\u7a76\u53ca\u5176\u4f18\u7f3a\u70b9\uff1b\u7b2c5\u8282\u63a2\u8ba8\u4e2d\u6587\u5b9e\u4f53\u5173\u7cfb\u62bd\u53d6</td></tr><tr><td>\u7684\u7814\u7a76\u73b0\u72b6\u548c\u65b9\u6cd5\uff1b\u7b2c6\u8282\u5bf9\u672c\u6587\u8fdb\u884c\u603b\u7ed3\u5e76\u5c55\u671b\u5173\u7cfb\u62bd\u53d6\u672a\u6765\u7684\u7814\u7a76\u65b9\u5411\u3002</td></tr><tr><td>2 \u6570 \u6570 \u6570\u636e \u636e \u636e\u96c6 \u96c6 \u96c6</td></tr><tr><td>2.1</td></tr></table>", | |
| "text": "\u7cfb\uff0c\u5f97\u5230(subject:\u4e3b\u8bed, relation:\u5173\u7cfb, object:\u5bbe\u8bed)\u4e09\u5143\u7ec4\uff0c\u5176\u4e2dsubject\u548cobject\u8868\u793a\u4e24\u4e2a\u5b9e \u6709 \u6709 \u6709\u76d1 \u76d1 \u76d1\u7763 \u7763 \u7763\u6570 \u6570 \u6570\u636e \u636e \u636e\u96c6 \u96c6 \u96c6 \u6709\u76d1\u7763\u7684\u6570\u636e\u96c6\u9700\u8981\u4eba\u5de5\u6807\u6ce8\uff0c\u610f\u5473\u7740\u6570\u636e\u5305\u542b\u9ad8\u8d28\u91cf\u7684\u5b9e\u4f53\u5173\u7cfb\u4e09\u5143\u7ec4\uff0c\u51e0\u4e4e\u6ca1\u6709\u566a \u97f3\u3002\u4f46\u662f\u4eba\u5de5\u6807\u6ce8\u6570\u636e\u96c6\u8017\u65f6\u8017\u529b\uff0c\u56e0\u6b64\u8fd9\u4e9b\u6570\u636e\u96c6\u901a\u5e38\u89c4\u6a21\u8f83\u5c0f\u3002\u4e0b\u9762\u4ecb\u7ecd\u7684\u4e24\u4e2a\u6570\u636e\u96c6 \u4e2d\uff0c\u6bcf\u4e2a\u53e5\u5b50\u4e2d\u7684\u547d\u540d\u5b9e\u4f53\u8fdb\u884c\u4e86\u6807\u8bb0\u5e76\u4e14\u5b9e\u4f53\u5bf9\u8868\u8fbe\u7684\u5173\u7cfb\u53ef\u4ee5\u88ab\u9884\u6d4b\u3002 ACE 2005\u6570 \u6570 \u6570\u636e \u636e \u636e\u96c6 \u96c6 \u96c6\uff1a \uff1a \uff1a \u81ea\u52a8\u5185\u5bb9\u62bd\u53d6\u6570\u636e\u96c6(ACE)\u5305\u542b599\u4e2a\u4e0e\u65b0\u95fb\u548c\u7535\u5b50\u90ae\u4ef6\u76f8\u5173\u7684\u6587\u6863\uff0c \u5e76\u5212\u5206\u4e3a7\u79cd\u4e3b\u8981\u7684\u5173\u7cfb\u7c7b\u578b\u3002\u5176\u4e2d\uff0c6\u79cd\u4e3b\u8981\u5173\u7cfb\u7c7b\u578b\u5305\u542b\u8db3\u591f\u7684\u5b9e\u4f8b\uff0c\u6bcf\u79cd\u5173\u7cfb\u7c7b\u578b\u5e73\u5747 \u6709700\u4e2a\u5b9e\u4f8b\u7528\u4e8e\u8bad\u7ec3\u548c\u6d4b\u8bd5\u3002 SemEval-2010 Task 8 \u6570 \u6570 \u6570\u636e \u636e \u636e\u96c6 \u96c6 \u96c6\uff1a \uff1a \uff1a \u8be5\u6570\u636e\u96c6", | |
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| "content": "<table><tr><td>\u566a\u97f3\u95ee\u9898\u3002\u5728\u591a\u793a\u4f8b\u8bad\u7ec3\u4e2d\u628a\u76ee\u6807\u51fd\u6570\u5b9a\u4e49\u5728\u5305\u4e0a\uff0c\u9996\u5148\u5bf9\u5305\u4e2d\u7684\u6bcf\u4e2a\u793a\u4f8b\u5206\u522b\u9884\u6d4b\uff0c\u5f97\u5230\u76f8 \u7684\u77e5\u8bc6\u6765\u63d0\u9ad8\u5c3e\u90e8\u6570\u636e\u8d2b\u4e4f\u7c7b\u7684\u6027\u80fd\u3002\u9996\u5148\uff0c\u4ed6\u4eec\u63d0\u51fa\u5229\u7528\u77e5\u8bc6\u56fe\u5d4c\u5165\u7684\u7c7b\u6807\u7b7e\u95f4\u7684\u9690\u5f0f\u5173\u7cfb</td></tr><tr><td>\u5e94\u7684\u5173\u7cfb\u6982\u7387\uff0c\u7136\u540e\u9009\u53d6\u6982\u7387\u6700\u5927\u7684\u793a\u4f8b\u6807\u7b7e\u4f5c\u4e3a\u5305\u7684\u6807\u7b7e\uff0c\u5e76\u5229\u7528\u5305\u7684\u6807\u7b7e\u66f4\u65b0\u7f51\u7edc\u53c2\u6570\u3002 \u77e5\u8bc6\uff0c\u5229\u7528\u56fe\u5377\u79ef\u7f51\u7edc\u5b66\u4e60\u663e\u5f0f\u5173\u7cfb\u77e5\u8bc6\u3002\u5176\u6b21\uff0c\u901a\u8fc7\u7c97\u5230\u7ec6\u7684\u77e5\u8bc6\u611f\u77e5\u6ce8\u610f\u673a\u5236\uff0c\u5c06\u5173\u8054\u77e5</td></tr><tr><td>PCNN\u5728\u8fdc\u7a0b\u76d1\u7763\u6570\u636e\u96c6\u4e0a\u5f97\u5230\u4e86\u4e0d\u9519\u7684\u6548\u679c\uff0c\u4f46\u8fd9\u79cd\u65b9\u6cd5\u4ecd\u7136\u6709\u7f3a\u9677\u3002\u9996\u5148\uff0cPCNN\u5c06 \u8bc6\u96c6\u6210\u5230\u5173\u8054\u62bd\u53d6\u6a21\u578b\u4e2d\u3002</td></tr><tr><td>\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u770b\u4f5c\u4e00\u4e2a\u5355\u6807\u7b7e\u5b66\u4e60\u95ee\u9898\uff0c\u5e76\u4e3a\u6bcf\u4e2a\u5b9e\u4f53\u5bf9\u9009\u62e9\u4e00\u4e2a\u5173\u7cfb\u6807\u7b7e\uff0c\u800c\u5ffd\u7565\u4e86\u540c \u4e00\u4e2a\u5b9e\u4f53\u5bf9\u53ef\u80fd\u5b58\u5728\u591a\u4e2a\u5173\u7cfb\u7684\u4e8b\u5b9e\u3002\u9488\u5bf9\u8fd9\u4e2a\u95ee\u9898\uff0c\u5229\u7528\u4e24\u4e2a\u4e0d\u540c\u7684\u635f\u5931\u51fd\u6570\uff0c (Jiang et 4.4 \u5c0f \u5c0f \u5c0f\u7ed3 \u7ed3 \u7ed3</td></tr><tr><td>al., 2016)\u5904\u7406\u591a\u6807\u7b7e\u5206\u7c7b\u95ee\u9898\u3002\u6b64\u5916\uff0cPCNN\u57fa\u4e8eRiedel\u63d0\u51fa\u7684\u5047\u8bbe\u6765\u751f\u6210\u6807\u8bb0\u6570\u636e,\u6839\u636e\u8fd9\u4e00 \u5728\u672c\u8282\u4e2d\uff0c\u6211\u4eec\u8be6\u7ec6\u8ba8\u8bba\u4e86\u4e00\u4e9b\u7528\u4e8e\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u7684\u7ecf\u5178\u65b9\u6cd5\uff0c\u4e0b\u9762\u88682\u7ed9\u51fa\u8fd9\u4e9b\u65b9\u6cd5</td></tr><tr><td>\u5047\u8bbe\uff0cPCNN\u5728\u8bad\u7ec3\u548c\u9884\u6d4b\u4e2d\u53ea\u9009\u62e9\u6bcf\u4e2a\u5b9e\u4f53\u5bf9\u53ef\u80fd\u7684\u53e5\u5b50\u3002\u7136\u800c\uff0c\u9009\u62e9\u4e00\u4e2a\u53e5\u5b50\u4f1a\u4e22\u5931\u5305\u542b \u5728NYT\u6570\u636e\u96c6\u4e0a\u62bd\u53d6\u5173\u7cfb\u793a\u4f8b\u524d100(TOP-100)\u3001\u524d200(Top-200)\u3001\u524d300(TOP-300)\u524d500(Top-</td></tr><tr><td>\u5176\u4ed6\u53e5\u5b50\u4e2d\u7684\u4fe1\u606f\u3002\u5bf9\u4e8e\u8fd9\u4e2a\u95ee\u9898\uff0c\u5047\u8bbe\"\u4e24\u4e2a\u5b9e\u4f53\u95f4\u7684\u5173\u7cfb\u53ef\u4ee5\u81ea\u52a8\u4ece\u63d0\u5230\u8fd9\u4e24\u4e2a\u5b9e\u4f53\u7684\u6240 500)\u7684\u5bf9\u6bd4\u7ed3\u679c,\u8fd9\u91cc\u4f7f\u7528Precision@N(P@N)\u4e3a\u8bc4\u4f30\u6307\u6807\u3002\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u53ea\u9700\u8981\u624b\u52a8\u6807\u6ce8</td></tr><tr><td>\u6709\u53e5\u5b50\u4e2d\u663e\u793a\u8868\u8fbe\u6216\u9690\u5f0f\u63a8\u65ad\" (Jiang et al., 2016)\uff0c\u5728\u4f7f\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u81ea\u52a8\u63d0\u53d6\u6bcf\u4e2a\u53e5\u5b50\u7684 \u5c11\u91cf\u7684\u5173\u7cfb\u5b9e\u4f8b\uff0c\u9002\u7528\u4e8e\u6ca1\u6709\u6807\u6ce8\u8bed\u6599\u5e93\u7684\u5173\u7cfb\u62bd\u53d6\uff0c\u4f46\u5176\u5b9e\u73b0\u8fc7\u7a0b\u5728\u6570\u636e\u96c6\u4e2d\u5f15\u5165\u4e86\u566a\u58f0\uff0c</td></tr><tr><td>\u7279\u5f81\u540e\uff0c\u4ed6\u4eec\u4f7f\u7528\u8de8\u53e5\u6700\u5927\u6c60\u5316\u6765\u9009\u62e9\u4e0d\u540c\u53e5\u5b50\u7684\u7279\u5f81\uff0c\u7136\u540e\u5c06\u6700\u91cd\u8981\u7684\u7279\u5f81\u805a\u5408\u4e3a\u6bcf\u4e2a\u5b9e\u4f53 \u4f7f\u5f97\u8be5\u65b9\u6cd5\u7684\u6027\u80fd\u4f4e\u4e8e\u6709\u76d1\u7763\u7684\u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\u3002\u8bb8\u591a\u540e\u7eed\u7684\u5de5\u4f5c\u90fd\u8bd5\u56fe\u5229\u7528\u9009\u62e9\u6027\u6ce8\u610f\u529b\u673a</td></tr><tr><td>\u5bf9\u7684\u8868\u793a\u3002\u7531\u4e8e\u7ed3\u679c\u8868\u793a\u6709\u4e0d\u540c\u53e5\u5b50\u7684\u7279\u5f81\u7ec4\u6210\uff0c\u56e0\u6b64\u8be5\u65b9\u6cd5\u5145\u5206\u5229\u7528\u8fd9\u4e9b\u53e5\u5b50\u4e2d\u5305\u542b\u7684\u6240\u6709 \u5236\u3001\u878d\u5408\u77e5\u8bc6\u5e93\u7b49\u65b9\u6cd5\u6765\u5904\u7406\u566a\u58f0\u548c\u653e\u5bbd\u8fdc\u7a0b\u76d1\u7763\u5047\u8bbe\uff0c\u901a\u8fc7\u53bb\u566a\u8fdb\u4e00\u6b65\u63d0\u9ad8\u6027\u80fd\u3002\u8fdc\u7a0b\u76d1\u7763</td></tr><tr><td>Socher et al., 2012) \u53ef\u7528\u4fe1\u606f\u3002\u6b64\u5916\uff0c\u5229\u7528\u53e5\u5b50\u7ea7\u522b\u7684\u6ce8\u610f\u529b\u673a\u5236 (Lin et al., 2016)\u6765\u81ea\u52a8\u6355\u83b7\u4e0d\u540c\u53e5\u5b50\u7684\u91cd\u8981\u7a0b Word embeddings+POS,NER, WordNet \u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\u5229\u7528\u7684\u662f\u5f31\u6807\u6ce8\u6570\u636e\uff0c\u4e00\u822c\u7684\u795e\u7ecf\u7f51\u7edc\u65b9\u6cd5\u90fd\u662f\u4ee5\u6570\u636e\u9a71\u52a8\u6a21\u578b\uff0c\u4f46\u7eaf\u6570\u636e\u9a71\u52a8 82.4% \u5ea6\uff0c\u8fc7\u6ee4\u566a\u58f0\u53e5\u5b50\u3002 \u6a21\u578b\u5e76\u4e0d\u80fd\u5145\u5206\u6316\u6398\u6570\u636e\u4e2d\u7684\u6f5c\u5728\u4fe1\u606f\uff0c\u4ece\u8868\u4e2d\u6211\u4eec\u53ef\u4ee5\u53d1\u73b0\uff0c\u878d\u5408\u77e5\u8bc6\u5e93\u7684\u65b9\u6cd5\u76f8\u5bf9\u6709\u7740\u59e3</td></tr><tr><td>Word embeddings + \u4e0a\u8ff0\u65b9\u6cd5\u4f7f\u7528\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u6a21\u578b\u5927\u591a\u662f\u76f8\u5bf9\u8f83\u6d45\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u901a\u5e38\u53ea\u6d89\u53ca\u4e00\u4e2a\u5377\u79ef\u5c42 \u597d\u7684\u6027\u80fd\uff0c\u8fd9\u7c7b\u65b9\u6cd5\u80fd\u591f\u66f4\u597d\u5730\u5c06\u7ed3\u6784\u5316\u77e5\u8bc6\u878d\u5165\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u4e2d\uff0c\u7528\u77e5\u8bc6\u6307\u5bfc\u6a21\u578b\u3002\u878d\u5408\u77e5 CNN word position embeddings, 82.7% \u548c\u4e00\u4e2a\u5168\u8fde\u63a5\u5c42\uff0c\u800c\u4e14\u4e0d\u6e05\u695a\u66f4\u6df1\u7684\u6a21\u578b\u7ed3\u6784\u662f\u5426\u80fd\u591f\u4ece\u566a\u58f0\u6570\u636e\u4e2d\u63d0\u53d6\u4fe1\u53f7\u3002\u4e00\u79cd\u57fa\u4e8e\u6b8b\u5dee \u8bc6\u5e93\u7684\u65b9\u6cd5\uff0c\u4e0d\u4ec5\u4ec5\u662f\u5173\u7cfb\u62bd\u53d6\u4efb\u52a1\uff0c\u5728\u5176\u4ed6\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\u4e2d\u4e5f\u5177\u6709\u91cd\u8981\u610f\u4e49\u3002 (Zeng et al., 2014) WordNet \u878d \u878d \u878d\u5408 \u5408 \u5408\u8bcd \u8bcd \u8bcd\u6c47 \u6c47 \u6c47\u7279 \u7279 \u7279\u5f81 \u5f81 \u5f81\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 CR-CNN (Santos et al., 2015) Word embeddings + word position embeddings 84.1% \u5b66\u4e60\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u3002 (Huang et al., 2017)\u7528\u4e8e\u5173\u7cfb\u62bd\u53d6\uff0c\u4ed6\u4eec\u5c06\u8bcd\u5d4c\u5165\u548c\u4f4d\u7f6e\u5d4c\u5165\u5408\u5e76\u5230\u4e00 \u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5\u5206 \u5206 \u5206\u7c7b \u7c7b \u7c7b \u6a21 \u6a21 \u6a21\u578b \u578b \u578b Top-100 Top-200 Top-300 Top-500 \u4e2a\u6df1\u5ea6\u6b8b\u5dee\u7f51\u7edc\u4e2d\uff0c\u901a\u8fc7\u6052\u7b49\u6620\u5c04\u5230\u5377\u79ef\u5c42\u4e2d\u3002\u5b9e\u9a8c\u8868\u660e\uff0c\u8be5\u65b9\u6cd5\u5229\u75289\u5c42\u5e26\u6b8b\u5dee\u5b66\u4e60\u7684\u5377\u79ef\u795e \u7ecf\u7f51\u7edc\u53ef\u4ee5\u663e\u8457\u63d0\u5347\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u6027\u80fd\u3002 PCNN</td></tr><tr><td>depLCNN (Xu et al., 2015(a)) SDP-LSTM (Xu et al., 2015(b)) BRCNN (Cai et al., 2016) C-GCN (Zhang et al., 2018) 4.2 \u540e\uff0c\u6240\u6709\u53e5\u5b50\u7279\u5f81\u5411\u91cf\u7684\u52a0\u6743\u6c42\u548c\u5c31\u662f\u5305\u7684\u7279\u5f81\u3002\u6b64\u5916\uff0c\u4e3a\u4e86\u5c06\u66f4\u591a\u7684\u80cc\u666f\u77e5\u8bc6\u878d\u5165\u5230\u6a21\u578b WordNet, words around nominals 85.6% Word Embeddings, POS embeddings, WordNet embeddings, grammar relation embeddings 83.7% Word embeddings+POS, NER, WordNet embeddings 86.3% Word embeddings 84.8% \u4e2d\uff0c\u8be5\u65b9\u6cd5\u4f7f\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6765\u63d0\u53d6\u5b9e\u4f53\u63cf\u8ff0\u7684\u7279\u5f81\u5411\u91cf\u3002 \u57fa \u57fa AGGCNs (Guo et al., 2019) Word embeddings \u4f7f\u7528\u6ce8\u610f\u529b\u673a\u5236\uff0c\u662f\u4e00\u79cd\u901a\u8fc7\u5b66\u4e60\u591a\u4e2a\u793a\u4f8b\u7684\u6743\u503c\u5206\u5e03\u6765\u9009\u62e9\u6709\u6548\u793a\u4f8b\u7684\u65b9\u6cd5\u3002\u4f46\u662f\uff0c\u57fa 85.7% \u4e8e\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u7684\u8fdc\u7a0b\u76d1\u7763\u5b66\u4e60\u4e2d\u5b58\u5728\u4e24\u4e2a\u91cd\u8981\u7684\u8868\u793a\u5b66\u4e60\u95ee\u9898\uff1a(1)\u5728\u4e00\u4e2a\u793a\u4f8b\u4e2d\uff0c\u76ee\u6807\u5b9e\u4f53</td></tr><tr><td>AttBLSTM (Zhou et al., 2016) \u5bf9\u4e0a\u4e0b\u6587\u8868\u793a\u5b66\u4e60\u95ee\u9898\uff1b(2)\u591a\u4e2a\u793a\u4f8b\u7684\u6709\u6548\u793a\u4f8b\u9009\u62e9\u8868\u793a\u5b66\u4e60\u3002\u5728\u5148\u524d\u7684\u7814\u7a76\u5de5\u4f5c\u4e2d\uff0c\u901a\u5e38\u91c7 Word embeddings, position embeddings 84.0% \u75281-D\u5411\u91cf\u7684\u5355\u8bcd\u7ea7\u548c\u53e5\u5b50\u7ea7\u6ce8\u610f\u529b\u673a\u5236\u30021-D\u6ce8\u610f\u529b\u5411\u91cf\u7684\u7f3a\u9677\u662f\uff0c\u5b83\u53ea\u5173\u6ce8\u53e5\u5b50\u4e2d\u4e00\u4e2a\u6216\u5c11</td></tr><tr><td>\u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u6ce8 \u6ce8 \u6ce8\u610f \u610f \u610f\u529b \u529b \u529b\u673a \u673a \u673a\u5236 \u5236 \u5236\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 \u91cf\u7684\u65b9\u9762\uff0c\u6216\u4e00\u4e2a\u6216\u5c11\u91cf\u7684\u793a\u4f8b\u3002\u5176\u7ed3\u679c\u662f\u4e0d\u540c\u8bed\u4e49\u65b9\u9762\u7684\u53e5\u5b50\uff0c\u6216\u8005\u4e0d\u540c\u7684\u591a\u4e2a\u6709\u6548\u53e5\u5b50\u88ab BiLSTM with Entity-aware attention (Lee et al., 2019) Word emneddings, 85.2% \u5ffd\u7565\u3002\u53d7\u7ed3\u6784\u5316\u81ea\u6ce8\u610f\u529b\u53e5\u5b50\u5d4c\u5165 (Lin et al., 2017)\u7684\u542f\u53d1\uff0c\u4e00\u79cd\u65b0\u7684\u57fa\u4e8e\u53cc\u5411LSTM\u7684\u591a\u5c42\u7ed3 Latent entity Typing \u6784\u5316\u81ea\u6ce8\u610f\u529b\u673a\u5236\u6a21\u578b(MLSSA) (Du et al., 2018)\u7528\u4e8e\u7f13\u89e3\u4e0a\u8ff0\u4e24\u4e2a\u95ee\u9898\u3002\u9488\u5bf9\u7b2c\u4e00\u4e2a\u95ee\u9898\uff0c</td></tr><tr><td>\u4ed6\u4eec\u63d0\u51fa\u4e00\u4e2a\u57fa\u4e8e\u4e8c\u7ef4\u77e9\u9635\u7684\u5355\u8bcd\u7ea7\u6ce8\u610f\u529b\u673a\u5236\uff0c\u8be5\u673a\u5236\u5305\u542b\u591a\u4e2a\u5411\u91cf\uff0c\u6bcf\u4e2a\u5411\u91cf\u90fd\u805a\u7126\u4e8e\u53e5 Table 1: \u6709\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\u5728\u6570\u636e\u96c6SemEval-2010\u5bf9\u6bd4 \u5b50\u7684\u4e0d\u540c\u65b9\u9762\uff0c\u4ece\u800c\u66f4\u597d\u5730\u5b66\u4e60\u4e0a\u4e0b\u6587\u8868\u793a\u3002\u9488\u5bf9\u7b2c\u4e8c\u4e2a\u95ee\u9898\uff0c\u4ed6\u4eec\u63d0\u51fa\u4e00\u79cd\u7528\u4e8e\u591a\u793a\u4f8b\u5b66\u4e60</td></tr><tr><td>\u7684\u4e8c\u7ef4\u53e5\u5b50\u7ea7\u6ce8\u610f\u529b\u673a\u5236\uff0c\u5176\u4e2d\u5305\u542b\u591a\u4e2a\u5411\u91cf\uff0c\u6bcf\u4e2a\u5411\u91cf\u90fd\u96c6\u4e2d\u5728\u4e0d\u540c\u7684\u6709\u6548\u793a\u4f8b\u4e0a\uff0c\u4ee5\u66f4\u597d</td></tr><tr><td>\u5730\u9009\u62e9\u53e5\u5b50\u3002 4 \u57fa \u57fa \u6709\u76d1\u7763\u7684\u5173\u7cfb\u62bd\u53d6\u9700\u8981\u4f9d\u8d56\u4eba\u5de5\u6807\u6ce8\u7684\u6570\u636e\u96c6\uff0c\u9650\u5236\u4e86\u8be5\u65b9\u6cd5\u7684\u9002\u7528\u9886\u57df\u3002\u56e0\u6b64\uff0c\u8fdc\u7a0b\u76d1 4.3 \u878d \u878d \u878d\u5408 \u5408 \u5408\u77e5 \u77e5 \u77e5\u8bc6 \u8bc6 \u8bc6\u5e93 \u5e93 \u5e93\u7684 \u7684 \u7684\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5</td></tr><tr><td>\u7763\u65b9\u6cd5 (Mintz et al., 2009)\u5c06\u6587\u6863\u4e0e\u5df2\u77e5\u7684\u77e5\u8bc6\u5e93\u5bf9\u9f50\uff0c\u7528\u4e8e\u81ea\u52a8\u751f\u6210\u5927\u91cf\u8bad\u7ec3\u6570\u636e\u3002\u7136\u800c\uff0c \u4e3a\u4e86\u7f13\u89e3\u8fdc\u7a0b\u76d1\u7763\u4e2d\u9519\u8bef\u6807\u6ce8\u95ee\u9898\uff0c\u8bb8\u591a\u7814\u7a76\u5229\u7528\u73b0\u6709\u77e5\u8bc6\u5e93\u6dfb\u52a0\u4fe1\u606f\u3002\u9996\u5148\uff0c\u4e00\u79cd\u65e0\u6807</td></tr><tr><td>\u8fdc\u7a0b\u76d1\u7763\u5047\u8bbe\u662f\u4e00\u4e2a\u5f3a\u5047\u8bbe\u5e76\u4e14\u4f1a\u5bfc\u81f4\u9519\u8bef\u6807\u7b7e\u95ee\u9898\uff0c\u5373\u63d0\u5230\u4e24\u4e2a\u5b9e\u4f53\u7684\u53e5\u5b50\u4e0d\u4e00\u5b9a\u8868\u8fbe\u4ed6 \u7b7e\u7684\u8fdc\u7a0b\u76d1\u7763\u65b9\u6cd5 (Wang et al., 2018)\u5728\u8ddd\u79bb\u5047\u8bbe\u4e0d\u5145\u5206\u7684\u6761\u4ef6\u4e0b\uff0c\u4e0d\u4f7f\u7528\u5173\u7cfb\u6807\u7b7e\uff0c\u53ea\u5229\u7528</td></tr><tr><td>\u4eec\u5728\u77e5\u8bc6\u5e93\u4e2d\u7684\u5173\u7cfb\u3002\u56e0\u6b64\u53ef\u4ee5\u5c06\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u4efb\u52a1\u4f5c\u4e3a\u4e00\u4e2a\u591a\u793a\u4f8b\u5b66\u4e60\u95ee\u9898\u6765\u653e\u5bbd\u5047 \u77e5\u8bc6\u5e93(KG)\u7684\u5148\u9a8c\u77e5\u8bc6\u76f4\u63a5\u3001\u67d4\u548c\u7684\u76d1\u7763\u5206\u7c7b\u5668\u7684\u5b66\u4e60\u3002\u9664\u4e86\u5173\u7cfb\u793a\u4f8b\u5916\uff0c\u77e5\u8bc6\u5e93\u4e2d\u8fd8\u5305\u62ec\u5176</td></tr><tr><td>\u8bbe (Riedel et al., 2010)\u3002\u5728\u7528\u4e8e\u5173\u7cfb\u62bd\u53d6\u7684\u591a\u793a\u4f8b\u5b66\u4e60\u4e2d\uff0c\u77e5\u8bc6\u5e93(KB)\u4e2d\u7684\u6bcf\u4e2a\u5b9e\u4f53\u5bf9\u6807\u8bb0\u4e00 \u4ed6\u76f8\u5173\u4fe1\u606f\uff0c\u6bd4\u5982\u5173\u7cfb\u7684\u522b\u540d,\u73b0\u6709\u7684\u5173\u7cfb\u62bd\u53d6\u6a21\u578b\u901a\u5e38\u5ffd\u7565\u8fd9\u4e9b\u53ef\u7528\u7684\u4fe1\u606f\u3002\u4e00\u79cd\u8fdc\u7a0b\u76d1\u7763\u5173</td></tr><tr><td>\u4e2a\u53e5\u5b50\u5305\u3002\u5305\u4e2d\u7684\u6240\u6709\u53e5\u5b50\u90fd\u5305\u542b\u5b9e\u4f53\u5bf9\u7684\u63d0\u53ca\uff0c\u4f46\u5b83\u4eec\u4e0d\u4e00\u5b9a\u5305\u542b\u76f4\u63a5\u5173\u7cfb\u3002\u591a\u793a\u4f8b\u5b66\u4e60\u662f \u7cfb\u62bd\u53d6\u65b9\u6cd5-RESIDE (Vashishth et al., 2018)\u5229\u7528\u77e5\u8bc6\u5e93\u4e2d\u9644\u52a0\u7684\u8fb9\u4fe1\u606f\u6539\u8fdb\u5173\u7cfb\u62bd\u53d6\u3002\u5177\u4f53</td></tr><tr><td>\u5bf9\u5305\u6807\u7b7e\u9884\u6d4b\uff0c\u800c\u4e0d\u662f\u4e3a\u6bcf\u4e2a\u53e5\u5b50\u9884\u6d4b\u5173\u7cfb\u6807\u7b7e\u3002\u5b83\u5047\u5b9a\uff0c\u5982\u679c\u5b9e\u4f53\u5bf9\u5b58\u5728\u5173\u7cfb\uff0c\u5219\u5305\u4e2d\u81f3\u5c11 \u7684\uff0c\u5b83\u4f7f\u7528\u5b9e\u4f53\u7c7b\u578b\u548c\u5173\u7cfb\u522b\u540d\u4fe1\u606f\u5728\u9884\u6d4b\u5173\u7cfb\u65f6\u65bd\u52a0\u8f6f\u7ea6\u675f\uff0c\u4f7f\u7528\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4ece\u6587\u672c\u4e2d</td></tr><tr><td>\u6709\u4e00\u4e2a\u793a\u4f8b\u53cd\u6620\u7ed9\u5b9a\u5b9e\u4f53\u5bf9\u7684\u5173\u7cfb\u3002 \u7f16\u7801\u8bed\u6cd5\u4fe1\u606f\uff0c\u5373\u4f7f\u5728\u6709\u9650\u989d\u5916\u4fe1\u606f\u53ef\u7528\u65f6\u4e5f\u80fd\u63d0\u9ad8\u6027\u80fd\u3002</td></tr><tr><td>\u8fdc\u7a0b\u76d1\u7763\u53ef\u4ee5\u81ea\u52a8\u6807\u6ce8\u8db3\u591f\u6570\u91cf\u7684\u8bad\u7ec3\u6570\u636e;\u7136\u800c\uff0c\u8fd9\u4e9b\u6570\u636e\u901a\u5e38\u53ea\u8986\u76d6\u5173\u7cfb\u7684\u6709\u9650\u90e8\u5206\u3002</td></tr><tr><td>\u8bb8\u591a\u5173\u7cfb\u90fd\u662f\u957f\u5c3e\u5173\u7cfb\uff0c\u6570\u636e\u4ecd\u7136\u4e0d\u8db3\u3002\u76ee\u524d\u7684\u8fdc\u7a0b\u76d1\u7763\u6a21\u578b\u5ffd\u7565\u4e86\u957f\u5c3e\u5173\u7cfb\u95ee\u9898\uff0c\u96be\u4ee5\u4ece\u7eaf 4.1 Riedel\u7684\u65b9\u6cd5\u62bd\u53d6\u6587\u672c\u7279\u5f81\u65f6\uff0c\u9700\u8981\u4f9d\u8d56\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5de5\u5177\uff0c\u4f1a\u9020\u6210\u9519\u8bef\u4f20\u64ad\u95ee\u9898\u3002\u56e0 \u6587\u672c\u4e2d\u63d0\u53d6\u51fa\u5168\u9762\u7684\u4fe1\u606f\u3002\u53d7\u5728\u5c3e\u90e8\u7684\u6570\u636e\u548c\u5728\u9876\u90e8\u7684\u6570\u636e\u4e4b\u95f4\u4e30\u5bcc\u7684\u8bed\u4e49\u5173\u8054\u7684\u542f\u53d1\uff0c\u4e00\u79cd</td></tr><tr><td>\u6b64\uff0c\u5206\u6bb5\u5377\u79ef\u7f51\u7edc(PCNN) (Zeng et al., 2015)\u7528\u6765\u63d0\u53d6\u7279\u5f81\uff0c\u5e76\u5229\u7528\u591a\u793a\u4f8b\u5b66\u4e60\u65b9\u6cd5\u7f13\u89e3\u6570\u636e \u7528\u4e8e\u957f\u5c3e\u4e0d\u5e73\u8861\u6570\u636e\u7684\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u63d0\u53d6\u65b9\u6cd5 (Zhang et al., 2019)\u5229\u7528\u5206\u5e03\u9876\u90e8\u6570\u636e\u4e30\u5bcc\u7684\u7c7b</td></tr></table>", | |
| "text": "\u57fa\u4e8e \u4e8e \u4e8e\u6700 \u6700 \u6700\u77ed \u77ed \u77ed\u4f9d \u4f9d \u4f9d\u5b58 \u5b58 \u5b58\u8def \u8def \u8def\u5f84 \u5f84 \u5f84\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 \u57fa\u4e8e \u4e8e \u4e8e\u8fdc \u8fdc \u8fdc\u7a0b \u7a0b \u7a0b\u76d1 \u76d1 \u76d1\u7763 \u7763 \u7763\u7684 \u7684 \u7684\u5173 \u5173 \u5173\u7cfb \u7cfb \u7cfb\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5\u7814 \u7814 \u7814\u7a76 \u7a76 \u7a76 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u5377 \u5377 \u5377\u79ef \u79ef \u79ef\u795e \u795e \u795e\u7ecf \u7ecf \u7ecf\u7f51 \u7f51 \u7f51\u7edc \u7edc \u7edc\u7684 \u7684 \u7684\u8fdc \u8fdc \u8fdc\u7a0b \u7a0b \u7a0b\u76d1 \u76d1 \u76d1\u7763 \u7763 \u7763\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u6ce8 \u6ce8 \u6ce8\u610f \u610f \u610f\u529b \u529b \u529b\u673a \u673a \u673a\u5236 \u5236 \u5236\u7684 \u7684 \u7684\u8fdc \u8fdc \u8fdc\u7a0b \u7a0b \u7a0b\u76d1 \u76d1 \u76d1\u7763 \u7763 \u7763\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 \u73b0\u6709\u65b9\u6cd5\u5728\u9009\u62e9\u6709\u6548\u793a\u4f8b\u548c\u7f3a\u4e4f\u5b9e\u4f53\u80cc\u666f\u77e5\u8bc6\u65b9\u9762\u5b58\u5728\u7f3a\u9677\uff0c\u4e00\u4e2a\u57fa\u4e8ePCNN\u7684\u53e5\u5b50\u7ea7\u6ce8 \u610f\u529b\u673a\u5236\u6a21\u578b(APCNNs)(Ji et al., 2017)\u7528\u6765\u9009\u62e9\u6709\u6548\u793a\u4f8b\uff0c\u8be5\u6a21\u578b\u5145\u5206\u5229\u7528\u4e86\u77e5\u8bc6\u5e93\u4e2d\u7684\u76d1 \u7763\u4fe1\u606f\u3002\u4ed6\u4eec\u4eceFreebase\u548cWikipedia\u9875\u9762\u4e2d\u63d0\u53d6\u5b9e\u4f53\u63cf\u8ff0\u6765\u8865\u5145\u80cc\u666f\u77e5\u8bc6\u3002\u5bf9\u4e8e\u4e00\u4e2a\u5305\uff0c\u6a21 \u578b\u9996\u5148\u4f7f\u7528PCNNs\u63d0\u53d6\u6bcf\u4e2a\u53e5\u5b50\u7684\u7279\u5f81\u5411\u91cfv sen \u3002\u53d7\u5230TransE\u6a21\u578b\u7684\u542f\u53d1\uff0c\u5728TransE\u6a21\u578b\u4e2d\uff0c \u7528e 1 + r \u2248 e 2 \u5bf9\u4e00\u4e2a\u4e09\u5143\u7ec4r(e 1 1, e 2 )\u5efa\u6a21\uff0c\u5728APCNNs\u4e2d\uff0c\u7528(e 1 \u2212 e 2 )\u8868\u793a\u53e5\u5b50\u4e2de 1 \u548ce 2 \u4e4b\u95f4\u7684 \u5173\u7cfb\u3002\u7136\u540e\uff0c\u6a21\u578b\u901a\u8fc7\u4e00\u4e2a\u9690\u542b\u5c42\u7528\u4e32\u8054[v sen ; e 1 \u2212 e 2 ]\u7684\u65b9\u5f0f\u8ba1\u7b97\u6bcf\u4e2a\u53e5\u5b50\u7684\u6ce8\u610f\u529b\u6743\u91cd\u3002\u6700", | |
| "html": null | |
| }, | |
| "TABREF3": { | |
| "type_str": "table", | |
| "num": null, | |
| "content": "<table><tr><td>\u5b9e\u4f53\u548c\u5173\u7cfb\u8054\u5408\u62bd\u53d6\u5728\u4e8e\u4ece\u975e\u7ed3\u6784\u5316\u6587\u672c\u4e2d\u540c\u65f6\u8fdb\u884c\u5b9e\u4f53\u8bc6\u522b\u548c\u5173\u7cfb\u62bd\u53d6\u3002\u4f20\u7edf\u65b9\u6cd5\u4ee5\u6d41</td></tr><tr><td>\u6c34\u7ebf\u65b9\u5f0f\u5904\u7406\u62bd\u53d6\u5b9e\u4f53\u5173\u7cfb\u4e09\u5143\u7ec4\u4efb\u52a1\uff0c\u5373\u9996\u5148\u63d0\u53d6\u5b9e\u4f53\uff0c\u7136\u540e\u8bc6\u522b\u4ed6\u4eec\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u8fd9\u4e2a\u72ec</td></tr><tr><td>\u7acb\u7684\u6846\u67b6\u4f7f\u4efb\u52a1\u6613\u4e8e\u5904\u7406\uff0c\u5e76\u4e14\u6bcf\u4e2a\u7ec4\u4ef6\u90fd\u53ef\u4ee5\u66f4\u52a0\u7075\u6d3b\u3002\u4f46\u5b83\u5ffd\u7565\u4e86\u8fd9\u4e24\u4e2a\u5b50\u4efb\u52a1\u4e4b\u95f4\u7684\u76f8</td></tr><tr><td>\u5173\u6027\uff0c\u5728\u8fd9\u79cd\u65b9\u5f0f\u4e0b\uff0c\u6bcf\u4e2a\u5b50\u4efb\u52a1\u90fd\u662f\u72ec\u7acb\u7684\u6a21\u578b\u3002\u8fd9\u6837\uff0c\u5b9e\u4f53\u8bc6\u522b\u7684\u7ed3\u679c\u53ef\u80fd\u4f1a\u5f71\u54cd\u5173\u7cfb\u5206</td></tr><tr><td>\u7c7b\u7684\u6027\u80fd\uff0c\u5bfc\u81f4\u9519\u8bef\u4f20\u9012\u3002\u4e0e\u6d41\u6c34\u7ebf\u65b9\u6cd5\u4e0d\u540c\uff0c\u8054\u5408\u5b66\u4e60\u6846\u67b6\u80fd\u5229\u7528\u5355\u4e2a\u6a21\u578b\u63d0\u53d6\u5b9e\u4f53\u548c\u5173</td></tr><tr><td>\u7cfb\uff0c\u80fd\u591f\u6709\u6548\u5730\u96c6\u6210\u5b9e\u4f53\u548c\u5173\u7cfb\u7684\u4fe1\u606f\u3002</td></tr><tr><td>5.1</td></tr></table>", | |
| "text": "\u8fdc\u7a0b\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\u65b9\u6cd5\u5728\u6570\u636e\u96c6NYT\u6570\u636e\u96c6\u5bf9\u6bd4 5 \u5b9e \u5b9e \u5b9e\u4f53 \u4f53 \u4f53\u548c \u548c \u548c\u5173 \u5173 \u5173\u7cfb \u7cfb \u7cfb\u8054 \u8054 \u8054\u5408 \u5408 \u5408\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5\u7814 \u7814 \u7814\u7a76 \u7a76 \u7a76 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u5171 \u5171 \u5171\u4eab \u4eab \u4eab\u53c2 \u53c2 \u53c2\u6570 \u6570 \u6570\u7684 \u7684 \u7684\u8054 \u8054 \u8054\u5408 \u5408 \u5408\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5 \u6700\u65e9\u7684\u8054\u5408\u6846\u67b6\u6a21\u578b (Li et al., 2014)\u5229\u7528\u7ed3\u6784\u5316\u611f\u77e5\u673a\u548c\u96c6\u675f\u641c\u7d22\u65b9\u5f0f\u540c\u65f6\u63d0\u53d6\u5b9e\u4f53\u53ca\u5176 \u5173\u7cfb\u3002\u8be5\u6846\u67b6\u91c7\u7528\u4e86\u4e00\u79cd\u57fa\u4e8e\u534a\u9a6c\u5c14\u79d1\u592b\u94fe\u601d\u60f3\u7684\u5206\u6bb5\u89e3\u7801\u5668\uff0c\u514b\u670d\u4e86\u4f20\u7edf\u7684\u57fa\u4e8e\u5b57\u7b26\u7684\u6807\u6ce8 \u65b9\u5f0f\u3002\u6b64\u5916\uff0c\u8003\u8651\u5230\u4e0d\u7cbe\u786e\u7684\u641c\u7d22\uff0c\u4ed6\u4eec\u63d0\u51fa\u4e00\u4e9b\u65b0\u7684\u6709\u6548\u7684\u5168\u5c40\u7279\u5f81\u4f5c\u4e3a\u7ea6\u675f\u6765\u6355\u6349\u5b9e\u4f53\u548c \u5173\u7cfb\u4e4b\u95f4\u7684\u76f8\u4e92\u4f9d\u8d56\u6027\u3002 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u5168 \u5168 \u5168\u5c40 \u5c40 \u5c40\u4f18 \u4f18 \u4f18\u5316 \u5316 \u5316\u7684 \u7684 \u7684\u8054 \u8054 \u8054\u5408 \u5408 \u5408\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5", | |
| "html": null | |
| }, | |
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| "num": null, | |
| "content": "<table><tr><td>COAE2016\uff1a \uff1a \uff1a \u8be5\u6570\u636e\u96c6\u6765\u6e90\u4e8e\u7b2c\u516b\u5c4a\u4e2d\u6587\u503e\u5411\u6027\u5206\u6790\u8bc4\u6d4b(COAE2016)\u7684\u9762\u5411\u77e5\u8bc6\u62bd\u53d6\u7684</td></tr><tr><td>\u5173\u7cfb\u5206\u7c7b\u4efb\u52a1\u3002\u8be5\u6570\u636e\u96c6\u5305\u542b988\u53e5\u8bad\u7ec3\u96c6\u3001483\u53e5\u6d4b\u8bd5\u96c6\uff0c\u4ee5\u53ca10\u79cd\u5173\u7cfb\u7c7b\u578b(\u4eba\u7269\u7684\u51fa\u751f\u65e5</td></tr><tr><td>\u671f, \u4eba\u7269\u7684\u51fa\u751f\u5730, \u4eba\u7269\u7684\u6bd5\u4e1a\u9662\u6821, \u4eba\u7269\u7684\u914d\u5076, \u4eba\u7269\u7684\u5b50\u5973, \u7ec4\u7ec7\u673a\u6784\u7684\u9ad8\u7ba1, \u7ec4\u7ec7\u673a\u6784\u7684\u5458\u5de5</td></tr><tr><td>\u6570, \u7ec4\u7ec7\u673a\u6784\u7684\u521b\u59cb\u4eba, \u7ec4\u7ec7\u673a\u6784\u7684\u6210\u7acb\u65f6\u95f4, \u7ec4\u7ec7\u673a\u6784\u7684\u603b\u90e8\u5730\u70b9)</td></tr><tr><td>ACE2005\uff1a \uff1a \uff1a ACE 2005\u6570\u636e\u96c6\u6536\u96c6\u81ea\u65b0\u95fb\u4e13\u7ebf\u3001\u5e7f\u64ad\u548c\u7f51\u7edc\u65e5\u5fd7\u3002\u5173\u7cfb\u5206\u4e3a\uff16\u5927\u7c7b\u548c18\u4e2a\u5c0f</td></tr><tr><td>\u7c7b\uff0c\u5305\u542b8023\u4e2a\u5173\u7cfb\u4e8b\u5b9e\u548c18\u4e2a\u5173\u7cfb\u5b50\u7c7b\u578b\u3002</td></tr><tr><td>DuIE\uff1a \uff1a \uff1a 2019\u5e74\uff0c\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u3001\u4e2d\u56fd\u4e2d\u6587\u4fe1\u606f\u5b66\u4f1a\u8054\u5408\u767e\u5ea6\u516c\u53f8\u4e3e\u529e\u7684\u8bed\u8a00\u4e0e\u667a\u80fd\u6280\u672f\u7ade</td></tr><tr><td>\u8d5b\u5f00\u653e\u4e86\u57fa\u4e8e\u767e\u5ea6\u767e\u79d1\u548c\u767e\u5ea6\u4fe1\u606f\u6d41\u7684\u5927\u89c4\u6a21\u4e2d\u6587\u4fe1\u606f\u62bd\u53d6\u6570\u636e\u96c6 (Li et al., 2019)\u3002\u8be5\u6570\u636e\u96c6</td></tr></table>", | |
| "text": "\u5b9e\u4f53\u5173\u7cfb\u8054\u5408\u62bd\u53d6\u5728\u4e0d\u540c\u636e\u96c6\u7684\u7ed3\u679c 6 \u4e2d \u4e2d \u4e2d\u6587 \u6587 \u6587\u5b9e \u5b9e \u5b9e\u4f53 \u4f53 \u4f53\u5173 \u5173 \u5173\u7cfb \u7cfb \u7cfb\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u7814 \u7814 \u7814\u7a76 \u7a76 \u7a76\u73b0 \u73b0 \u73b0\u72b6 \u72b6 \u72b6 \u5728\u4e2d\u6587\u7814\u7a76\u65b9\u9762\uff0c\u7531\u4e8e\u6807\u6ce8\u8bed\u6599\u7684\u77ed\u7f3a\uff0c\u5173\u7cfb\u62bd\u53d6\u7684\u7814\u7a76\u76f8\u5bf9\u4e8e\u82f1\u6587\u6570\u636e\u96c6\u4e0a\u7684\u7814\u7a76\u8f83 \u5c11\u3002\u672c\u6587\u805a\u7126\u795e\u7ecf\u7f51\u7edc\u7684\u4e2d\u6587\u5b9e\u4f53\u5173\u7cfb\u62bd\u53d6\u7814\u7a76\uff0c\u4f20\u7edf\u65b9\u6cd5\u53ef\u53c2\u8003 (\u6b66\u6587\u96c5 et al., 2018)\u7684\u5de5 \u4f5c\uff0c\u8fdc\u7a0b\u76d1\u7763\u65b9\u6cd5\u53ef\u53c2\u8003 (\u767d\u9f99 et al., 2019)\u7684\u5de5\u4f5c\u3002 6.1 \u4e2d \u4e2d \u4e2d\u6587 \u6587 \u6587\u5b9e \u5b9e \u5b9e\u4f53 \u4f53 \u4f53\u5173 \u5173 \u5173\u7cfb \u7cfb \u7cfb\u62bd \u62bd \u62bd\u53d6 \u53d6 \u53d6\u6570 \u6570 \u6570\u636e \u636e \u636e\u96c6 \u96c6 \u96c6 \u57fa \u57fa \u57fa\u4e8e \u4e8e \u4e8e\u795e \u795e \u795e\u7ecf \u7ecf \u7ecf\u7f51 \u7f51 \u7f51\u7edc \u7edc \u7edc\u65b9 \u65b9 \u65b9\u6cd5 \u6cd5 \u6cd5\u7814 \u7814 \u7814\u7a76 \u7a76 \u7a76\u73b0 \u73b0 \u73b0\u72b6 \u72b6 \u72b6 \u4e3a\u4e86\u5728\u4e2d\u6587\u8bed\u6599\u4e2d\u83b7\u5f97\u66f4\u4e30\u5bcc\u7684\u9ad8\u7ea7\u7279\u5f81\uff0c\u9ad8\u5c42\u8bed\u4e49\u6ce8\u610f\u529b\u673a\u5236\u7684\u5206\u6bb5\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u6a21 \u578b(\u6b66\u6587\u96c5 et al., 2019)\u7528\u4e8e\u4e2d\u6587\u5173\u7cfb\u62bd\u53d6\u3002\u5728\u6a21\u578b\u7684\u5411\u91cf\u8868\u793a\u4e2d\uff0c\u6dfb\u52a0\u4e86HowNet\u4e2d\u7684\u4e0a\u4f4d\u8bcd", | |
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| } | |
| } | |
| } | |
| } |