USING BERT FOR Attribute Extraction in KnowledgeGraph. fine-tuning and feature extraction. 使用基于bert的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取。
USING BERT FOR Attribute Extraction in KnowledgeGraph with two method,fine-tuning and feature extraction.
知识图谱百度百科人物词条属性抽取,使用基于bert的微调fine-tuning和特征提取feature-extraction方法进行实验。
Tensorflow >=1.10
scikit-learn
BERT-Base, Chinese
:
Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M
parameters
None
The dataset is constructed according to Baidu Encyclopedia character entries. Filter out corpus that does not contain entities and attributes.
Entities and attributes are obtained from name entity recognition.
Labels are obtained from the Baidu Encyclopedia infobox, and most of them are labeled manually,so some are not very good.
For example:
黄维#1904年#1#黄维(1904年-1989年),字悟我,出生于江西贵溪一农户家庭。
陈昂#山东省滕州市#1#邀请担任诗词嘉宾。1992年1月26日,陈昂出生于山东省滕州市一个普通的知识分子家庭,其祖父、父亲都
陈伟庆#肇庆市鼎湖区#0#长。任免信息2016年10月21日下午,肇庆市鼎湖区八届人大一次会议胜利闭幕。陈伟庆当选区人民政府副区长。
strip.py
can get striped datadata_process.py
can process data to get numpy file inputparameters
file is the parameters that run model needFor example with birthplace dataset:
fine-tuning
run_classifier.py
to get predicted probability outputspython run_classifier.py \
--task_name=my \
--do_train=true \
--do_predict=true \
--data_dir=a \
--vocab_file=/home/tiny/zhaomeng/bertmodel/vocab.txt \
--bert_config_file=/home/tiny/zhaomeng/bertmodel/bert_config.json \
--init_checkpoint=/home/tiny/zhaomeng/bertmodel/bert_model.ckpt \
--max_seq_length=80 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=1.0 \
--output_dir=./output
proba2metrics.py
to get final result with wrong classificationfeature-extraction
extract_features.py
to get the vector representation of train and test data in json file formatpython extract_features.py \
--input_file=../data/birth_place_train.txt \
--output_file=../data/birth_place_train.jsonl \
--vocab_file=/home/tiny/zhaomeng/bertmodel/vocab.txt \
--bert_config_file=/home/tiny/zhaomeng/bertmodel/bert_config.json \
--init_checkpoint=/home/tiny/zhaomeng/bertmodel/bert_model.ckpt \
--layers=-1 \
--max_seq_length=80 \
--batch_size=16
json2vector.py
to transfer json file to vector representationrun_classifier.py
to use machine learning methods to do classification,MLP usually peforms bestThe predicted results and misclassified corpus are saved in result dir.
For example with birthplace dataset using fine-tuning method,the result is:
precision recall f1-score support
0 0.963 0.967 0.965 573
1 0.951 0.946 0.948 389
This project is licensed under the MIT License