0. 摘要
参加了天池的一个pdf简历信息提取的比赛，这里进行回顾、整理和分享
赛题要求从pdf简历中提取出信息，比如说名字，籍贯等。这里搭建了一个BiLSTM-CRF模型，能够从PDF简历中提取出所需的信息。
模型的线上得分是0.727，排名 21/1200+
1. 赛题相关
模型目标：pdf简历 --> 类别信息
2. 思路
使用python库pdfminer，将pdf简历中的文本提取出来。利用json标注文件，对提取出来的文本进行匹配和BIO标注，每一个字对应一个标注。最后，将标注后的文本送到BiLSM-CRF模型中进行训练。
3. BiLSTM-CRF 模型
将文本中的每个字进行one-hot编码，经过Embedding层后，每一个字对应一个字向量，所以文本可以用一个矩阵表示。将文本矩阵输入BiLSTM层，输出中每一个字会对应一个类别概率向量，此类别概率向量表示了该字属于各个类别的概率。所以所有字属于各个类别的概率可以用一个类别概率矩阵表示。将此类别概率矩阵输入CRF层，即可得到得分最高的文本标注序列。
此处留一个pytorch官方的BiLSTM-CRF教程链接： https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html#
4. 代码地址
https://github.com/Agwave/PDF-Resume-Information-Extraction
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