NLP入门（四）命名实体识别（NER）

阳光穿透心脏的1/2处 2022-04-16 00:30 503阅读 0赞

本文将会简单介绍自然语言处理（NLP）中的命名实体识别（NER）。  
  命名实体识别（Named Entity Recognition，简称NER）是信息提取、问答系统、句法分析、机器翻译等应用领域的重要基础工具，在自然语言处理技术走向实用化的过程中占有重要地位。一般来说，命名实体识别的任务就是识别出待处理文本中三大类（实体类、时间类和数字类）、七小类（人名、机构名、地名、时间、日期、货币和百分比）命名实体。  
  举个简单的例子，在句子“小明早上8点去学校上课。”中，对其进行命名实体识别，应该能提取信息

> 人名：小明，时间：早上8点，地点：学校。

本文将会介绍几个工具用来进行命名实体识别，后续有机会的话，我们将会尝试着用HMM、CRF或深度学习来实现命名实体识别。  
  首先我们来看一下NLTK和Stanford NLP中对命名实体识别的分类，如下图：

![20191219223810610.png][]

在上图中，LOCATION和GPE有重合。GPE通常表示地理—政治条目，比如城市，州，国家，洲等。LOCATION除了上述内容外，还能表示名山大川等。FACILITY通常表示知名的纪念碑或人工制品等。  
  下面介绍两个工具来进行NER的任务：NLTK和Stanford NLP。  
  首先是NLTK，我们的示例文档（介绍FIFA，来源于维基百科）如下：

> FIFA was founded in 1904 to oversee international competition among the national associations of Belgium,  
> Denmark, France, Germany, the Netherlands, Spain, Sweden, and Switzerland. Headquartered in Zürich, its  
> membership now comprises 211 national associations. Member countries must each also be members of one of  
> the six regional confederations into which the world is divided: Africa, Asia, Europe, North & Central America  
> and the Caribbean, Oceania, and South America.

实现NER的Python代码如下：

import re
    import pandas as pd
    import nltk
    
    def parse_document(document):
       document = re.sub('\n', ' ', document)
       if isinstance(document, str):
           document = document
       else:
           raise ValueError('Document is not string!')
       document = document.strip()
       sentences = nltk.sent_tokenize(document)
       sentences = [sentence.strip() for sentence in sentences]
       return sentences
    
    # sample document
    text = """ FIFA was founded in 1904 to oversee international competition among the national associations of Belgium, Denmark, France, Germany, the Netherlands, Spain, Sweden, and Switzerland. Headquartered in Zürich, its membership now comprises 211 national associations. Member countries must each also be members of one of the six regional confederations into which the world is divided: Africa, Asia, Europe, North & Central America and the Caribbean, Oceania, and South America. """
    
    # tokenize sentences
    sentences = parse_document(text)
    tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
    # tag sentences and use nltk's Named Entity Chunker
    tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
    ne_chunked_sents = [nltk.ne_chunk(tagged) for tagged in tagged_sentences]
    # extract all named entities
    named_entities = []
    for ne_tagged_sentence in ne_chunked_sents:
       for tagged_tree in ne_tagged_sentence:
           # extract only chunks having NE labels
           if hasattr(tagged_tree, 'label'):
               entity_name = ' '.join(c[0] for c in tagged_tree.leaves()) #get NE name
               entity_type = tagged_tree.label() # get NE category
               named_entities.append((entity_name, entity_type))
               # get unique named entities
               named_entities = list(set(named_entities))
    
    # store named entities in a data frame
    entity_frame = pd.DataFrame(named_entities, columns=['Entity Name', 'Entity Type'])
    # display results
    print(entity_frame)

输出结果如下：

Entity Name   Entity Type
    0              FIFA  ORGANIZATION
    1   Central America  ORGANIZATION
    2           Belgium           GPE
    3         Caribbean      LOCATION
    4              Asia           GPE
    5            France           GPE
    6           Oceania           GPE
    7           Germany           GPE
    8     South America           GPE
    9           Denmark           GPE
    10           Zürich           GPE
    11           Africa        PERSON
    12           Sweden           GPE
    13      Netherlands           GPE
    14            Spain           GPE
    15      Switzerland           GPE
    16            North           GPE
    17           Europe           GPE

可以看到，NLTK中的NER任务大体上完成得还是不错的，能够识别FIFA为组织（ORGANIZATION），Belgium,Asia为GPE, 但是也有一些不太如人意的地方，比如，它将Central America识别为ORGANIZATION，而实际上它应该为GPE；将Africa识别为PERSON，实际上应该为GPE。

接下来，我们尝试着用Stanford NLP工具。关于该工具，我们主要使用Stanford NER 标注工具。在使用这个工具之前，你需要在自己的电脑上安装Java（一般是JDK），并将Java添加到系统路径中，同时下载英语NER的文件包：stanford-ner-2018-10-16.zip（大小为172MB），下载地址为：https://nlp.stanford.edu/software/CRF-NER.shtml 。以笔者的电脑为例，Java所在的路径为：C:\\Program Files\\Java\\jdk1.8.0\_161\\bin\\java.exe， 下载Stanford NER的zip文件解压后的文件夹的路径为：E://stanford-ner-2018-10-16，如下图所示：

![2019121922383453.png][]

在classifer文件夹中有如下文件：

![20191219223840997.png][]

它们代表的含义如下：

> 3 class: Location, Person, Organization  
> 4 class: Location, Person, Organization, Misc  
> 7 class: Location, Person, Organization, Money, Percent, Date, Time

可以使用Python实现Stanford NER，完整的代码如下：

import re
    from nltk.tag import StanfordNERTagger
    import os
    import pandas as pd
    import nltk
    
    def parse_document(document):
       document = re.sub('\n', ' ', document)
       if isinstance(document, str):
           document = document
       else:
           raise ValueError('Document is not string!')
       document = document.strip()
       sentences = nltk.sent_tokenize(document)
       sentences = [sentence.strip() for sentence in sentences]
       return sentences
    
    # sample document
    text = """ FIFA was founded in 1904 to oversee international competition among the national associations of Belgium, Denmark, France, Germany, the Netherlands, Spain, Sweden, and Switzerland. Headquartered in Zürich, its membership now comprises 211 national associations. Member countries must each also be members of one of the six regional confederations into which the world is divided: Africa, Asia, Europe, North & Central America and the Caribbean, Oceania, and South America. """
    
    sentences = parse_document(text)
    tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
    
    # set java path in environment variables
    java_path = r'C:\Program Files\Java\jdk1.8.0_161\bin\java.exe'
    os.environ['JAVAHOME'] = java_path
    # load stanford NER
    sn = StanfordNERTagger('E://stanford-ner-2018-10-16/classifiers/english.muc.7class.distsim.crf.ser.gz',
                           path_to_jar='E://stanford-ner-2018-10-16/stanford-ner.jar')
    
    # tag sentences
    ne_annotated_sentences = [sn.tag(sent) for sent in tokenized_sentences]
    # extract named entities
    named_entities = []
    for sentence in ne_annotated_sentences:
       temp_entity_name = ''
       temp_named_entity = None
       for term, tag in sentence:
           # get terms with NE tags
           if tag != 'O':
               temp_entity_name = ' '.join([temp_entity_name, term]).strip() #get NE name
               temp_named_entity = (temp_entity_name, tag) # get NE and its category
           else:
               if temp_named_entity:
                   named_entities.append(temp_named_entity)
                   temp_entity_name = ''
                   temp_named_entity = None
    
    # get unique named entities
    named_entities = list(set(named_entities))
    # store named entities in a data frame
    entity_frame = pd.DataFrame(named_entities, columns=['Entity Name', 'Entity Type'])
    # display results
    print(entity_frame)

输出结果如下：

Entity Name   Entity Type
    0                      1904          DATE
    1                   Denmark      LOCATION
    2                     Spain      LOCATION
    3   North & Central America  ORGANIZATION
    4             South America      LOCATION
    5                   Belgium      LOCATION
    6                    Zürich      LOCATION
    7           the Netherlands      LOCATION
    8                    France      LOCATION
    9                 Caribbean      LOCATION
    10                   Sweden      LOCATION
    11                  Oceania      LOCATION
    12                     Asia      LOCATION
    13                     FIFA  ORGANIZATION
    14                   Europe      LOCATION
    15                   Africa      LOCATION
    16              Switzerland      LOCATION
    17                  Germany      LOCATION

可以看到，在Stanford NER的帮助下，NER的实现效果较好，将Africa识别为LOCATION，将1904识别为时间（这在NLTK中没有识别出来），但还是对North & Central America识别有误，将其识别为ORGANIZATION。  
  值得注意的是，并不是说Stanford NER一定会比NLTK NER的效果好，两者针对的对象，预料，算法可能有差异，因此，需要根据自己的需求决定使用什么工具。  
  本次分享到此结束，以后有机会的话，将会尝试着用HMM、CRF或深度学习来实现命名实体识别。

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~~

[20191219223810610.png]: /images/20220416/a4f1b4cd02274aeb9e3d6ca5706895e4.png
[2019121922383453.png]: /images/20220416/533d6eb50cda466985ca09273679b93f.png
[20191219223840997.png]: /images/20220416/1069a7a4af8a4267b5c7aee5ff0fd1b9.png