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Chinese NER using Lattice LSTM. Reproduction for ACL 2018 paper.

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支持批并行的LatticeLSTM

运行环境:

  • python >= 3.7.3
  • fastNLP >= dev.0.5.0
  • pytorch >= 1.1.0
  • numpy >= 1.16.4
  • fitlog >= 0.2.0

支持的数据集:

未包含的数据集可以通过提供增加类似 load_data.py 中 load_ontonotes4ner 这个输出格式的函数来增加对其的支持

性能:

数据集 目前达到的F1分数(test) 原文中的F1分数(test)
Weibo 58.66(可能有误) 58.79
Resume 95.18 94.46
Ontonote 73.62 73.88

备注:Weibo数据集我用的是V2版本,也就是更新过的版本,根据杨杰博士Github上LatticeLSTM仓库里的某个issue,应该是一致的。

如有任何疑问请联系:


Batch Parallel LatticeLSTM

Environment:

  • python >= 3.7.3
  • fastNLP >= dev.0.5.0
  • pytorch >= 1.1.0
  • numpy >= 1.16.4
  • fitlog >= 0.2.0

Dataset:

  • Resume,downloaded from here
  • Ontonote
  • Weibo

to those unincluded dataset, you can write the interface function whose output form is like load_ontonotes4ner in load_data.py

Performance:

Dataset F1 of my code(test) F1 in paper(test)
Weibo 58.66(maybe wrong) 58.79
Resume 95.18 94.46
Ontonote 73.62 73.88

PS:The Weibo dataset I use is V2, namely revised version.

If any confusion, please contact:

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Chinese NER using Lattice LSTM. Reproduction for ACL 2018 paper.

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