代码拉取完成,页面将自动刷新
同步操作将从 natural-language-processing/DeepIE 强制同步,此操作会覆盖自 Fork 仓库以来所做的任何修改,且无法恢复!!!
确定后同步将在后台操作,完成时将刷新页面,请耐心等待。
DeepIE: 基于深度学习的信息抽取技术(预计2020年8月31日前全部更新完毕)
lexicon | Ontonotes | MSRA | Resume | ||
---|---|---|---|---|---|
biLSTM | ---- | 71.81 | 91.87 | 94.41 | 56.75 |
Lattice LSTM | 词表1 | 73.88 | 93.18 | 94.46 | 58.79 |
WC-LSTM | 词表1 | 74.43 | 93.36 | 94.96 | 49.86 |
LR-CNN | 词表1 | 74.45 | 93.71 | 95.11 | 59.92 |
CGN | 词表2 | 74.79 | 93.47 | 94.12 | 63.09 |
LGN | 词表1 | 74.85 | 93.63 | 95.41 | 60.15 |
Simple-Lexicon | 词表1 | 75.54 | 93.50 | 95.59 | 61.24 |
FLAT | 词表1 | 76.45 | 94.12 | 95.45 | 60.32 |
FLAT | 词表2 | 75.70 | 94.35 | 94.93 | 63.42 |
BERT | ---- | 80.14 | 94.95 | 95.53 | 68.20 |
BERT+FLAT | 词表1 | 81.82 | 96.09 | 95.86 | 68.55 |
方法 | f | p | r |
---|---|---|---|
char+ lstm-crf | 86.18% | 88.43% | 83.10% |
char-bigram + lstm-crf | 91.80% | 92.60% | 90.34% |
char-bigram + adTransformer-crf | 92.98% | 93.25% | 92.72% |
char-bigram + lexion-augment + lstm-crf | 93.33% | 94.26% | 92.43% |
char-bigram-BERT + lstm-crf | 94.71% | 95.14% | 94.27% |
char-bigram-BERT + lexion-augment + lstm-crf | 95.26% | 95.90% | 94.63% |
方法 | f | p | r |
---|---|---|---|
char-bigram + lstm-crf | 81.76% | 82.91% | 80.6 |
+ domain transfer(from ccks2018 to 2019) | 82.54% | 83.43% | 81.81% |
char-bigram + adTransformer-crf | 82.83% | 82.19% | 83.49% |
char-bigram + lexion-augment + lstm-crf | 82.76% | 82.79% | 82.72% |
BERT-finetune+crf | 83.49% | 84.11% | 82.89% |
roBERTa-finetune+crf | 83.66% | 83.67% | 83.66% |
char-bigram-BERT + lstm-crf | 83.37% | 83.51% | 83.22% |
char-bigram-BERT + lexion-augment + lstm-crf | 84.15% | 84.29% | 84.01% |
(注:测试集与ccks2019一致,去除ccks2020训练集中已经在2019测试集中的样本,下列指标未做规则处理和模型融合)
方法 | f | p | r |
---|---|---|---|
char-bigram + lstm-crf | 82.68% | 83.14% | 82.22% |
char-bigram + lexion-augment + lstm-crf | 83.12% | 83.10% | 83.14% |
char-bigram-BERT + lstm-crf | 83.12% | 83.04% | 83.21% |
char-bigram-BERT-RoBerta_wwm + lstm-crf | 83.66% | 83.76% | 83.56% |
char-bigram-BERT-XLNet + lstm-crf | 84.12% | 83.88% | 84.36% |
char-bigram-BERT + lexion-augment + lstm-crf | 84.50% | 84.32% | 84.67% |
方法 | f(dev) | p(dev) | r(dev) |
---|---|---|---|
multi head selection | 76.36 | 79.24 | 73.69 |
ETL-BIES | 77.07% | 77.13% | 77.06% |
ETL-Span | 78.94% | 80.11% | 77.8% |
ETL-Span + word2vec | 79.99% | 80.62% | 79.38% |
ETL-Span + word2vec + adversarial training | 80.38% | 79.95% | 80.82% |
ETL-Span + BERT | 81.88% | 82.35% | 81.42% |
方法 | f(dev) | p(dev) | r(dev) |
---|---|---|---|
ETL-Span + BERT | 74.58 | 74.44 | 74.71 |
# 药物-属性
['药品-用药频率','药品-持续时间','药品-用药剂量','药品-用药方法','药品-不良反应']
# 疾病-属性
['疾病-检查方法','疾病-临床表现','疾病-非药治疗','疾病-药品名称','疾病-部位']
主体 | 方法 | f | p | r |
---|---|---|---|---|
疾病 | lstm+ multi-label pointer network | 76.55 | 74.36 | 78.86 |
疾病 | bert + multi-label pointer network | 77.59 | 77.45 | 77.74 |
药物 | lstm+ multi-label pointer network | 81.12 | 79.15 | 83.19 |
CCKS2020-医疗事件抽取
CCKS2020:面向金融领域的篇章级事件主体抽取
CCKS2020:面向金融领域的篇章级事件要素抽取
信息抽取领域的数据资源汇总:
信息抽取相关竞赛汇总:
摘要抽取
前沿技术在信息抽取中的应用
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。