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relation-extraction

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wangwei921216
wangwei921216 commented Nov 5, 2019

Testing started at 上午 11:10 ...
F:\python\Anaconda3\envs\tensorflow\python.exe "F:\Program Files\PyCharm 2018.3.3\helpers\pycharm_jb_pytest_runner.py" --path F:/WorkSpace/python/Information-Extraction-Chinese-master/RE_BGRU_2ATT/test_GRU.py
Launching pytest with arguments F:/WorkSpace/python/Information-Extraction-Chinese-master/RE_BGRU_2ATT/test_GRU.py in F:\WorkSpace\python\Information-Extrac

基于Pytorch和torchtext的自然语言处理深度学习框架,包含序列标注、文本分类、句子关系、文本生成、结构分析、五大功能模块,已实现了命名实体识别、中文分词、词性标注、语义角色标注、情感分析、关系抽取、语言模型、文本相似度、文本蕴含、依存句法分析、词向量训练、聊天机器人、机器翻译、文本摘要等功能。框架功能丰富,开箱可用,极易上手!基本都是学习他人实现然后自己修改融合到框架中,没有细致调参,且有不少Bug~

  • Updated Jan 10, 2020
  • Python
BrambleXu
BrambleXu commented Jul 23, 2019

一句话总结:

针对abstractive text summarization task的seq2seq模型有两个缺点:重现的细节不准确,经常重复自己。

这篇文章我们提出一个框架来增强seq2seq, in two orthogonal ways。首先提出一个a hybrid pointer-generator network将source text里的word准确pointing到结果中去,并能通过generator保持产生novel words的能力。第二点,使用coverage来减少repitiion的情况。

资源:

Relation-Classification-using-Bidirectional-LSTM-Tree

TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations

  • Updated Apr 15, 2019
  • Jupyter Notebook

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