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：
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 nltkdef 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：
在classifer文件夹中有如下文件：
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 nltkdef 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。
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