NLP 基于kashgari和BERT实现中文命名实体识别（NER）

准备工作，先准备 python 环境，下载 BERT 语言模型

• Python 3.6 环境

需要安装kashgari

TensorFlow 2.x	pip install ‘kashgari>=2.0.0’coming soon
TensorFlow 1.14+	pip install ‘kashgari>=1.0.0,<2.0.0’current version
Keras	pip install ‘kashgari<1.0.0’legacy version

• BERT, Chinese 中文模型
  
  我选择的是工大的BERT-wwm-ext模型

在此感谢上述作者

数据集准备

from kashgari.corpus import ChineseDailyNerCorpus

train_x, train_y = ChineseDailyNerCorpus.load_data('train')

valid_x, valid_y = ChineseDailyNerCorpus.load_data('validate')

test_x, test_y  = ChineseDailyNerCorpus.load_data('test')

print(f"train data count: {len(train_x)}")

print(f"validate data count: {len(valid_x)}")

print(f"test data count: {len(test_x)}")

train data count: 20864

validate data count: 2318

test data count: 4636

采用人民日报标注的数据集，格式为：

海 O

钓 O

比 O

赛 O

地 O

点 O

在 O

厦 B-LOC

门 I-LOC

与 O

金 B-LOC

门 I-LOC

之 O

间 O

的 O

海 O

域 O

。 O

创建 BERT embedding

import kashgari

from kashgari.embeddings import BERTEmbedding

bert_embed = BERTEmbedding('chinese_wwm_ext_L-12_H-768_A-12',

                           task=kashgari.LABELING,

                           sequence_length=100)

创建模型并训练

from kashgari.tasks.labeling import BiLSTM_CRF_Model

# 还可以选择 `CNN_LSTM_Model`, `BiLSTM_Model`, `BiGRU_Model` 或 `BiGRU_CRF_Model`

model = BiLSTM_CRF_Model(bert_embed)

model.fit(train_x,

          train_y,

          x_validate=valid_x,

          y_validate=valid_y,

          epochs=20,

          batch_size=512)

model.save('ner.h5')

模型评估

model.evaluate(test_x, test_y)

BERT + B-LSTM-CRF 模型效果最好。详细得分如下：

LOC	0.9208	0.9324	0.9266
ORG	0.8728	0.8882	0.8804
PER	0.9622	0.9633	0.9627
avg / total	0.9169	0.9271	0.9220

模型使用

# -*- coding: utf-8 -*-

import kashgari

import re

loaded_model = kashgari.utils.load_model('per_ner.h5')

def cut_text(text, lenth):

    textArr = re.findall('.{' + str(lenth) + '}', text)

    textArr.append(text[(len(textArr) * lenth):])

    return textArr

def extract_labels(text, ners):

    ner_reg_list = []

    if ners:

        new_ners = []

        for ner in ners:

            new_ners += ner;

        for word, tag in zip([char for char in text], new_ners):

            if tag != 'O':

                ner_reg_list.append((word, tag))

    # 输出模型的NER识别结果

    labels = {}

    if ner_reg_list:

        for i, item in enumerate(ner_reg_list):

            if item[1].startswith('B'):

                label = ""

                end = i + 1

                while end <= len(ner_reg_list) - 1 and ner_reg_list[end][1].startswith('I'):

                    end += 1

                ner_type = item[1].split('-')[1]

                if ner_type not in labels.keys():

                    labels[ner_type] = []

                label += ''.join([item[0] for item in ner_reg_list[i:end]])

                labels[ner_type].append(label)

    return labels

while True:

    text_input = input('sentence: ')

    texts = cut_text(text_input, 100)

    ners = loaded_model.predict([[char for char in text] for text in texts])

    print(ners)

    labels = extract_labels(text_input, ners)

    print(labels)

参考文献

Chinese-BERT-wwm：https://github.com/ymcui/Chinese-BERT-wwm

Kashgari：https://github.com/BrikerMan/Kashgari