搭建NER分类器——方法1（投票模型）

1 导入数据并初始化

数据集见评论区

import pandas as pd
import numpy as np

# 导入数据初始化
data = pd.read_csv('ner_dataset.csv', encoding='latin1' )

data = data.fillna(method='ffill')
data.tail(10)

输出

2 预定义数据结构

# 预定义数据结构
words = list(set(data['Word'].values)) # 单词表
print(words[:50]) 

n_words = len(words) # 查看单词总个数
n_words

输出

3 投票模型代码

from sklearn.base import BaseEstimator, TransformerMixin

class MajorityVotingTagger(BaseEstimator, TransformerMixin):
    
    def fit(self, X, y):
        """
        x: list of words
        y: list of tags
        """
        word2cnt = {}
        tags = []
        
        for x, t in zip(X, y):
            if t not in tags:
                tags.append(t)
                
            if x in word2cnt:
                if t in word2cnt[x]:
                    word2cnt[x][t] += 1
                else:
                    word2cnt[x][t] = 1
            else:
                word2cnt[x] = {t: 1}
        
        self.mjvote = {}
        
        for k, d in word2cnt.items(): 
            #  k    :  d,      d
            # Indian: {B_gpe: 4, B_geo:1, ...}
            # 每个单词有哪些实体标签，{单词1:{实体名称1：次数， 实体名称2：次数}， 单词2:{实体名称1：次数， 实体名称2：次数}}
            self.mjvote[k] = max(d, key=d.get) # 取次数最多的实体名称
            
    def predict(self, X, y = None):
        """
        预测内存中的标签， 如果单词是未知的，则预测为O
        """
        return [self.mjvote.get(x, 'O') for x in X]            

4 数据预处理

words = data['Word'].values.tolist()
tags = data['Tag'].values.tolist()
print(words[:10], tags[:10], sep = '\n')

输出

5 模型预测

from sklearn.model_selection import cross_val_predict
from sklearn.metrics import classification_report

# 交叉验证
pred = cross_val_predict(estimator = MajorityVotingTagger(), X = words, y = tags, cv = 5)

# 计算验证报告
report = classification_report(y_pred = pred, y_true = tags)
print(report)

输出

本文地址：https://blog.csdn.net/qq_37150711/article/details/107676293