NLP入门(五)用深度学习实现命名实体识别(NER)

前言

  在文章:NLP入门(四)命名实体识别(NER)中,笔者介绍了两个实现命名实体识别的工具——NLTK和Stanford NLP。在本文中,我们将会学习到如何使用深度学习工具来自己一步步地实现NER,只要你坚持看完,就一定会很有收获的。
  OK,话不多说,让我们进入正题。
  几乎所有的NLP都依赖一个强大的语料库,本项目实现NER的语料库如下(文件名为train.txt,一共42000行,这里只展示前15行,可以在文章最后的Github地址下载该语料库):

played on Monday ( home team in CAPS ) :
VBD IN NNP ( NN NN IN NNP ) :
O O O O O O O O O O
American League
NNP NNP
B-MISC I-MISC
Cleveland 2 DETROIT 1
NNP CD NNP CD
B-ORG O B-ORG O
BALTIMORE 12 Oakland 11 ( 10 innings )
VB CD NNP CD ( CD NN )
B-ORG O B-ORG O O O O O
TORONTO 5 Minnesota 3
TO CD NNP CD
B-ORG O B-ORG O
......

简单介绍下该语料库的结构:该语料库一共42000行,每三行为一组,其中,第一行为英语句子,第二行为每个句子的词性(关于英语单词的词性,可参考文章:NLP入门(三)词形还原(Lemmatization)),第三行为NER系统的标注,具体的含义会在之后介绍。
  我们的NER项目的名称为DL_4_NER,结构如下:

项目中每个文件的功能如下:

  • utils.py: 项目配置及数据导入
  • data_processing.py: 数据探索
  • Bi_LSTM_Model_training.py: 模型创建及训练
  • Bi_LSTM_Model_predict.py: 对新句子进行NER预测

  接下来,笔者将结合代码文件,分部介绍该项目的步骤,当所有步骤介绍完毕后,我们的项目就结束了,而你,也就知道了如何用深度学习实现命名实体识别(NER)。
  Let's begin!

项目配置

  第一步,是项目的配置及数据导入,在utils.py文件中实现,完整的代码如下:

# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

# basic settings for DL_4_NER Project
BASE_DIR = "F://NERSystem"
CORPUS_PATH = "%s/train.txt" % BASE_DIR

KERAS_MODEL_SAVE_PATH = '%s/Bi-LSTM-4-NER.h5' % BASE_DIR
WORD_DICTIONARY_PATH = '%s/word_dictionary.pk' % BASE_DIR
InVERSE_WORD_DICTIONARY_PATH = '%s/inverse_word_dictionary.pk' % BASE_DIR
LABEL_DICTIONARY_PATH = '%s/label_dictionary.pk' % BASE_DIR
OUTPUT_DICTIONARY_PATH = '%s/output_dictionary.pk' % BASE_DIR

CONSTANTS = [
             KERAS_MODEL_SAVE_PATH,
             InVERSE_WORD_DICTIONARY_PATH,
             WORD_DICTIONARY_PATH,
             LABEL_DICTIONARY_PATH,
             OUTPUT_DICTIONARY_PATH
             ]

# load data from corpus to from pandas DataFrame
def load_data():
    with open(CORPUS_PATH, 'r') as f:
        text_data = [text.strip() for text in f.readlines()]
    text_data = [text_data[k].split('\t') for k in range(0, len(text_data))]
    index = range(0, len(text_data), 3)

    # Transforming data to matrix format for neural network
    input_data = list()
    for i in range(1, len(index) - 1):
        rows = text_data[index[i-1]:index[i]]
        sentence_no = np.array([i]*len(rows[0]), dtype=str)
        rows.append(sentence_no)
        rows = np.array(rows).T
        input_data.append(rows)

    input_data = pd.DataFrame(np.concatenate([item for item in input_data]),\
                               columns=['word', 'pos', 'tag', 'sent_no'])

    return input_data

在该代码中,先是设置了语料库文件的路径CORPUS_PATH,KERAS模型保存路径KERAS_MODEL_SAVE_PATH,以及在项目过程中会用到的三个字典的保存路径(以pickle文件形式保存)WORD_DICTIONARY_PATH,LABEL_DICTIONARY_PATH, OUTPUT_DICTIONARY_PATH。然后是load_data()函数,它将语料库中的文本以Pandas中的DataFrame结构展示出来,该数据框的前30行如下:

         word  pos     tag sent_no
0      played  VBD       O       1
1          on   IN       O       1
2      Monday  NNP       O       1
3           (    (       O       1
4        home   NN       O       1
5        team   NN       O       1
6          in   IN       O       1
7        CAPS  NNP       O       1
8           )    )       O       1
9           :    :       O       1
10   American  NNP  B-MISC       2
11     League  NNP  I-MISC       2
12  Cleveland  NNP   B-ORG       3
13          2   CD       O       3
14    DETROIT  NNP   B-ORG       3
15          1   CD       O       3
16  BALTIMORE   VB   B-ORG       4
17         12   CD       O       4
18    Oakland  NNP   B-ORG       4
19         11   CD       O       4
20          (    (       O       4
21         10   CD       O       4
22    innings   NN       O       4
23          )    )       O       4
24    TORONTO   TO   B-ORG       5
25          5   CD       O       5
26  Minnesota  NNP   B-ORG       5
27          3   CD       O       5
28  Milwaukee  NNP   B-ORG       6
29          3   CD       O       6

在该数据框中,word这一列表示文本语料库中的单词,pos这一列表示该单词的词性,tag这一列表示NER的标注,sent_no这一列表示该单词在第几个句子中。

数据探索

  接着,第二步是数据探索,即对输入的数据(input_data)进行一些数据review,完整的代码(data_processing.py)如下:

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

import pickle
import numpy as np
from collections import Counter
from itertools import accumulate
from operator import itemgetter
import matplotlib.pyplot as plt
import matplotlib as mpl
from utils import BASE_DIR, CONSTANTS, load_data

# 设置matplotlib绘图时的字体
mpl.rcParams['font.sans-serif']=['SimHei']

# 数据查看
def data_review():

    # 数据导入
    input_data = load_data()

    # 基本的数据review
    sent_num = input_data['sent_no'].astype(np.int).max()
    print("一共有%s个句子。\n"%sent_num)

    vocabulary = input_data['word'].unique()
    print("一共有%d个单词。"%len(vocabulary))
    print("前10个单词为:%s.\n"%vocabulary[:11])

    pos_arr = input_data['pos'].unique()
    print("单词的词性列表:%s.\n"%pos_arr)

    ner_tag_arr = input_data['tag'].unique()
    print("NER的标注列表:%s.\n" % ner_tag_arr)

    df = input_data[['word', 'sent_no']].groupby('sent_no').count()
    sent_len_list = df['word'].tolist()
    print("句子长度及出现频数字典:\n%s." % dict(Counter(sent_len_list)))

    # 绘制句子长度及出现频数统计图
    sort_sent_len_dist = sorted(dict(Counter(sent_len_list)).items(), key=itemgetter(0))
    sent_no_data = [item[0] for item in sort_sent_len_dist]
    sent_count_data = [item[1] for item in sort_sent_len_dist]
    plt.bar(sent_no_data, sent_count_data)
    plt.title("句子长度及出现频数统计图")
    plt.xlabel("句子长度")
    plt.ylabel("句子长度出现的频数")
    plt.savefig("%s/句子长度及出现频数统计图.png" % BASE_DIR)
    plt.close()

    # 绘制句子长度累积分布函数(CDF)
    sent_pentage_list = [(count/sent_num) for count in accumulate(sent_count_data)]

    # 寻找分位点为quantile的句子长度
    quantile = 0.9992
    #print(list(sent_pentage_list))
    for length, per in zip(sent_no_data, sent_pentage_list):
        if round(per, 4) == quantile:
            index = length
            break
    print("\n分位点为%s的句子长度:%d." % (quantile, index))

    # 绘制CDF
    plt.plot(sent_no_data, sent_pentage_list)
    plt.hlines(quantile, 0, index, colors="c", linestyles="dashed")
    plt.vlines(index, 0, quantile, colors="c", linestyles="dashed")
    plt.text(0, quantile, str(quantile))
    plt.text(index, 0, str(index))
    plt.title("句子长度累积分布函数图")
    plt.xlabel("句子长度")
    plt.ylabel("句子长度累积频率")
    plt.savefig("%s/句子长度累积分布函数图.png" % BASE_DIR)
    plt.close()

# 数据处理
def data_processing():
    # 数据导入
    input_data = load_data()

    # 标签及词汇表
    labels, vocabulary = list(input_data['tag'].unique()), list(input_data['word'].unique())

    # 字典列表
    word_dictionary = {word: i+1 for i, word in enumerate(vocabulary)}
    inverse_word_dictionary = {i+1: word for i, word in enumerate(vocabulary)}
    label_dictionary = {label: i+1 for i, label in enumerate(labels)}
    output_dictionary = {i+1: labels for i, labels in enumerate(labels)}

    dict_list = [word_dictionary, inverse_word_dictionary,label_dictionary, output_dictionary]

    # 保存为pickle形式
    for dict_item, path in zip(dict_list, CONSTANTS[1:]):
        with open(path, 'wb') as f:
            pickle.dump(dict_item, f)

#data_review()

调用data_review()函数,输出的结果如下:

一共有13998个句子。

一共有24339个单词。
前10个单词为:['played' 'on' 'Monday' '(' 'home' 'team' 'in' 'CAPS' ')' ':' 'American'].

单词的词性列表:['VBD' 'IN' 'NNP' '(' 'NN' ')' ':' 'CD' 'VB' 'TO' 'NNS' ',' 'VBP' 'VBZ'
 '.' 'VBG' 'PRP$' 'JJ' 'CC' 'JJS' 'RB' 'DT' 'VBN' '"' 'PRP' 'WDT' 'WRB'
 'MD' 'WP' 'POS' 'JJR' 'WP$' 'RP' 'NNPS' 'RBS' 'FW' '$' 'RBR' 'EX' "''"
 'PDT' 'UH' 'SYM' 'LS' 'NN|SYM'].

NER的标注列表:['O' 'B-MISC' 'I-MISC' 'B-ORG' 'I-ORG' 'B-PER' 'B-LOC' 'I-PER' 'I-LOC'
 'sO'].

句子长度及出现频数字典:
{1: 177, 2: 1141, 3: 620, 4: 794, 5: 769, 6: 639, 7: 999, 8: 977, 9: 841, 10: 501, 11: 395, 12: 316, 13: 339, 14: 291, 15: 275, 16: 225, 17: 229, 18: 212, 19: 197, 20: 221, 21: 228, 22: 221, 23: 230, 24: 210, 25: 207, 26: 224, 27: 188, 28: 199, 29: 214, 30: 183, 31: 202, 32: 167, 33: 167, 34: 141, 35: 130, 36: 119, 37: 105, 38: 112, 39: 98, 40: 78, 41: 74, 42: 63, 43: 51, 44: 42, 45: 39, 46: 19, 47: 22, 48: 19, 49: 15, 50: 16, 51: 8, 52: 9, 53: 5, 54: 4, 55: 9, 56: 2, 57: 2, 58: 2, 59: 2, 60: 3, 62: 2, 66: 1, 67: 1, 69: 1, 71: 1, 72: 1, 78: 1, 80: 1, 113: 1, 124: 1}.

分位点为0.9992的句子长度:60.

在该语料库中,一共有13998个句子,比预期的42000/3=14000个句子少两个。一个有24339个单词,单词量还是蛮大的,当然,这里对单词没有做任何处理,直接保留了语料库中的形式(后期可以继续优化)。单词的词性可以参考文章:NLP入门(三)词形还原(Lemmatization)。我们需要注意的是,NER的标注列表为['O' ,'B-MISC', 'I-MISC', 'B-ORG' ,'I-ORG', 'B-PER' ,'B-LOC' ,'I-PER', 'I-LOC','sO'],因此,本项目的NER一共分为四类:PER(人名),LOC(位置),ORG(组织)以及MISC,其中B表示开始,I表示中间,O表示单字词,不计入NER,sO表示特殊单字词。
  接下来,让我们考虑下句子的长度,这对后面的建模时填充的句子长度有有参考作用。句子长度及出现频数的统计图如下:

可以看到,句子长度基本在60以下,当然,这也可以在输出的句子长度及出现频数字典中看到。那么,我们是否可以选在一个标准作为后面模型的句子填充的长度呢?答案是,利用出现频数的累计分布函数的分位点,在这里,我们选择分位点为0.9992,对应的句子长度为60,如下图:

  接着是数据处理函数data_processing(),它的功能主要是实现单词、标签字典,并保存为pickle文件形式,便于后续直接调用。

建模

  在第三步中,我们建立Bi-LSTM模型来训练训练,完整的Python代码(Bi_LSTM_Model_training.py)如下:

# -*- coding: utf-8 -*-
import pickle
import numpy as np
import pandas as pd
from utils import BASE_DIR, CONSTANTS, load_data
from data_processing import data_processing
from keras.utils import np_utils, plot_model
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Bidirectional, LSTM, Dense, Embedding, TimeDistributed


# 模型输入数据
def input_data_for_model(input_shape):

    # 数据导入
    input_data = load_data()
    # 数据处理
    data_processing()
    # 导入字典
    with open(CONSTANTS[1], 'rb') as f:
        word_dictionary = pickle.load(f)
    with open(CONSTANTS[2], 'rb') as f:
        inverse_word_dictionary = pickle.load(f)
    with open(CONSTANTS[3], 'rb') as f:
        label_dictionary = pickle.load(f)
    with open(CONSTANTS[4], 'rb') as f:
        output_dictionary = pickle.load(f)
    vocab_size = len(word_dictionary.keys())
    label_size = len(label_dictionary.keys())

    # 处理输入数据
    aggregate_function = lambda input: [(word, pos, label) for word, pos, label in
                                            zip(input['word'].values.tolist(),
                                                input['pos'].values.tolist(),
                                                input['tag'].values.tolist())]

    grouped_input_data = input_data.groupby('sent_no').apply(aggregate_function)
    sentences = [sentence for sentence in grouped_input_data]

    x = [[word_dictionary[word[0]] for word in sent] for sent in sentences]
    x = pad_sequences(maxlen=input_shape, sequences=x, padding='post', value=0)
    y = [[label_dictionary[word[2]] for word in sent] for sent in sentences]
    y = pad_sequences(maxlen=input_shape, sequences=y, padding='post', value=0)
    y = [np_utils.to_categorical(label, num_classes=label_size + 1) for label in y]

    return x, y, output_dictionary, vocab_size, label_size, inverse_word_dictionary


# 定义深度学习模型:Bi-LSTM
def create_Bi_LSTM(vocab_size, label_size, input_shape, output_dim, n_units, out_act, activation):
    model = Sequential()
    model.add(Embedding(input_dim=vocab_size + 1, output_dim=output_dim,
                        input_length=input_shape, mask_zero=True))
    model.add(Bidirectional(LSTM(units=n_units, activation=activation,
                                 return_sequences=True)))
    model.add(TimeDistributed(Dense(label_size + 1, activation=out_act)))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model


# 模型训练
def model_train():

    # 将数据集分为训练集和测试集,占比为9:1
    input_shape = 60
    x, y, output_dictionary, vocab_size, label_size, inverse_word_dictionary = input_data_for_model(input_shape)
    train_end = int(len(x)*0.9)
    train_x, train_y = x[0:train_end], np.array(y[0:train_end])
    test_x, test_y = x[train_end:], np.array(y[train_end:])

    # 模型输入参数
    activation = 'selu'
    out_act = 'softmax'
    n_units = 100
    batch_size = 32
    epochs = 10
    output_dim = 20

    # 模型训练
    lstm_model = create_Bi_LSTM(vocab_size, label_size, input_shape, output_dim, n_units, out_act, activation)
    lstm_model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=1)

    # 模型保存
    model_save_path = CONSTANTS[0]
    lstm_model.save(model_save_path)
    plot_model(lstm_model, to_file='%s/LSTM_model.png' % BASE_DIR)

    # 在测试集上的效果
    N = test_x.shape[0]  # 测试的条数
    avg_accuracy = 0  # 预测的平均准确率
    for start, end in zip(range(0, N, 1), range(1, N+1, 1)):
        sentence = [inverse_word_dictionary[i] for i in test_x[start] if i != 0]
        y_predict = lstm_model.predict(test_x[start:end])
        input_sequences, output_sequences = [], []
        for i in range(0, len(y_predict[0])):
            output_sequences.append(np.argmax(y_predict[0][i]))
            input_sequences.append(np.argmax(test_y[start][i]))

        eval = lstm_model.evaluate(test_x[start:end], test_y[start:end])
        print('Test Accuracy: loss = %0.6f accuracy = %0.2f%%' % (eval[0], eval[1] * 100))
        avg_accuracy += eval[1]
        output_sequences = ' '.join([output_dictionary[key] for key in output_sequences if key != 0]).split()
        input_sequences = ' '.join([output_dictionary[key] for key in input_sequences if key != 0]).split()
        output_input_comparison = pd.DataFrame([sentence, output_sequences, input_sequences]).T
        print(output_input_comparison.dropna())
        print('#' * 80)

    avg_accuracy /= N
    print("测试样本的平均预测准确率:%.2f%%." % (avg_accuracy * 100))

model_train()

在上面的代码中,先是通过input_data_for_model()函数来处理好进入模型的数据,其参数为input_shape,即填充句子时的长度。然后是创建Bi-LSTM模型create_Bi_LSTM(),模型的示意图如下:

最后,是在输入的数据上进行模型训练,将原始的数据分为训练集和测试集,占比为9:1,训练的周期为10次。

模型训练

  运行上述模型训练代码,一共训练10个周期,训练时间大概为500s,在训练集上的准确率达99%以上,在测试集上的平均准确率为95%以上。以下是最后几个测试集上的预测结果:

......(前面的输出已忽略)
Test Accuracy: loss = 0.000986 accuracy = 100.00%
          0      1      2
0   Cardiff  B-ORG  B-ORG
1         1      O      O
2  Brighton  B-ORG  B-ORG
3         0      O      O
################################################################################

1/1 [==============================] - 0s 10ms/step
Test Accuracy: loss = 0.000274 accuracy = 100.00%
          0      1      2
0  Carlisle  B-ORG  B-ORG
1         0      O      O
2      Hull  B-ORG  B-ORG
3         0      O      O
################################################################################

1/1 [==============================] - 0s 9ms/step
Test Accuracy: loss = 0.000479 accuracy = 100.00%
           0      1      2
0    Chester  B-ORG  B-ORG
1          1      O      O
2  Cambridge  B-ORG  B-ORG
3          1      O      O
################################################################################

1/1 [==============================] - 0s 9ms/step
Test Accuracy: loss = 0.003092 accuracy = 100.00%
            0      1      2
0  Darlington  B-ORG  B-ORG
1           4      O      O
2     Swansea  B-ORG  B-ORG
3           1      O      O
################################################################################

1/1 [==============================] - 0s 8ms/step
Test Accuracy: loss = 0.000705 accuracy = 100.00%
             0      1      2
0       Exeter  B-ORG  B-ORG
1            2      O      O
2  Scarborough  B-ORG  B-ORG
3            2      O      O
################################################################################
测试样本的平均预测准确率:95.55%.

  该模型在原始数据上的识别效果还是可以的。
  训练完模型后,BASE_DIR中的所有文件如下:

模型预测

  最后,也许是整个项目最为激动人心的时刻,因为,我们要在新数据集上测试模型的识别效果。预测新数据的识别结果的完整Python代码(Bi_LSTM_Model_predict.py)如下:

# -*- coding: utf-8 -*-
# Name entity recognition for new data

# Import the necessary modules
import pickle
import numpy as np
from utils import CONSTANTS
from keras.preprocessing.sequence import pad_sequences
from keras.models import load_model
from nltk import word_tokenize

# 导入字典
with open(CONSTANTS[1], 'rb') as f:
    word_dictionary = pickle.load(f)
with open(CONSTANTS[4], 'rb') as f:
    output_dictionary = pickle.load(f)

try:
    # 数据预处理
    input_shape = 60
    sent = 'New York is the biggest city in America.'
    new_sent = word_tokenize(sent)
    new_x = [[word_dictionary[word] for word in new_sent]]
    x = pad_sequences(maxlen=input_shape, sequences=new_x, padding='post', value=0)

    # 载入模型
    model_save_path = CONSTANTS[0]
    lstm_model = load_model(model_save_path)

    # 模型预测
    y_predict = lstm_model.predict(x)

    ner_tag = []
    for i in range(0, len(new_sent)):
        ner_tag.append(np.argmax(y_predict[0][i]))

    ner = [output_dictionary[i] for i in ner_tag]
    print(new_sent)
    print(ner)

    # 去掉NER标注为O的元素
    ner_reg_list = []
    for word, tag in zip(new_sent, ner):
        if tag != 'O':
            ner_reg_list.append((word, tag))

    # 输出模型的NER识别结果
    print("NER识别结果:")
    if ner_reg_list:
        for i, item in enumerate(ner_reg_list):
            if item[1].startswith('B'):
                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]
                ner_type_dict = {'PER': 'PERSON: ',
                                'LOC': 'LOCATION: ',
                                'ORG': 'ORGANIZATION: ',
                                'MISC': 'MISC: '
                                }
                print(ner_type_dict[ner_type],\
                    ' '.join([item[0] for item in ner_reg_list[i:end]]))
    else:
        print("模型并未识别任何有效命名实体。")

except KeyError as err:
    print("您输入的句子有单词不在词汇表中,请重新输入!")
    print("不在词汇表中的单词为:%s." % err)

输出结果为:

['New', 'York', 'is', 'the', 'biggest', 'city', 'in', 'America', '.']
['B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O']
NER识别结果:
LOCATION:  New York
LOCATION:  America

  接下来,再测试三个笔者自己想的句子:

输入为:

sent = 'James is a world famous actor, whose home is in London.'

输出结果为:

['James', 'is', 'a', 'world', 'famous', 'actor', ',', 'whose', 'home', 'is', 'in', 'London', '.']
['B-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O']
NER识别结果:
PERSON:  James
LOCATION:  London

输入为:

sent = 'Oxford is in England, Jack is from here.'

输出为:

['Oxford', 'is', 'in', 'England', ',', 'Jack', 'is', 'from', 'here', '.']
['B-PER', 'O', 'O', 'B-LOC', 'O', 'B-PER', 'O', 'O', 'O', 'O']
NER识别结果:
PERSON:  Oxford
LOCATION:  England
PERSON:  Jack

输入为:

sent = 'I love Shanghai.'

输出为:

['I', 'love', 'Shanghai', '.']
['O', 'O', 'B-LOC', 'O']
NER识别结果:
LOCATION:  Shanghai

在上面的例子中,只有Oxford的识别效果不理想,模型将它识别为PERSON,其实应该是ORGANIZATION。

  接下来是三个来自CNN和wikipedia的句子:

输入为:

sent = "the US runs the risk of a military defeat by China or Russia"

输出为:

['the', 'US', 'runs', 'the', 'risk', 'of', 'a', 'military', 'defeat', 'by', 'China', 'or', 'Russia']
['O', 'B-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'B-LOC']
NER识别结果:
LOCATION:  US
LOCATION:  China
LOCATION:  Russia

输入为:

sent = "Home to the headquarters of the United Nations, New York is an important center for international diplomacy."

输出为:

['Home', 'to', 'the', 'headquarters', 'of', 'the', 'United', 'Nations', ',', 'New', 'York', 'is', 'an', 'important', 'center', 'for', 'international', 'diplomacy', '.']
['O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
NER识别结果:
ORGANIZATION:  United Nations
LOCATION:  New York

输入为:

sent = "The United States is a founding member of the United Nations, World Bank, International Monetary Fund."

输出为:

['The', 'United', 'States', 'is', 'a', 'founding', 'member', 'of', 'the', 'United', 'Nations', ',', 'World', 'Bank', ',', 'International', 'Monetary', 'Fund', '.']
['O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'O']
NER识别结果:
LOCATION:  United States
ORGANIZATION:  United Nations
ORGANIZATION:  World Bank
ORGANIZATION:  International Monetary Fund

  这三个例子识别全部正确。

总结

  到这儿,笔者的这个项目就差不多了。我们有必要对这个项目做个总结。
  首先是这个项目的优点。它的优点在于能够让你一步步地实现NER,而且除了语料库,你基本熟悉了如何创建一个识别NER系统的步骤,同时,对深度学习模型及其应用也有了深刻理解。因此,好处是显而易见的。当然,在实际工作中,语料库的整理才是最耗费时间的,能够占到90%或者更多的时间,因此,有一个好的语料库你才能展开工作。
  接着讲讲这个项目的缺点。第一个,是语料库不够大,当然,约14000条句子也够了,但本项目没有对句子进行文本预处理,所以,有些单词的变形可能无法进入词汇表。第二个,缺少对新词的处理,一旦句子中出现一个新的单词,这个模型便无法处理,这是后期需要完善的地方。第三个,句子的填充长度为60,如果输入的句子长度大于60,则后面的部分将无法有效识别。
  因此,后续还有更多的工作需要去做,当然,做一个中文NER也是可以考虑的。
  本项目已上传Github,地址为 https://github.com/percent4/D... 。:欢迎大家参考~

注意:本人现已开通微信公众号: Python爬虫与算法(微信号为:easy_web_scrape), 欢迎大家关注哦~~

参考文献

  1. BOOK: Applied Natural Language Processing with Python, Taweh Beysolow II
  2. WEBSITE:https://github.com/Apress/app...
  3. WEBSITE: NLP入门(四)命名实体识别(NER): https://www.jianshu.com/p/16e...
  4. WEBSITE:

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