NLP（五）命名实体识别（NER）

2024-07-03 15:09| 来源: 网络整理| 查看: 265

本文将会简单介绍自然语言处理（NLP）中的命名实体识别（NER）。

一，什么是命名实体识别 1，命名实体识别简介

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。

2，命名实体识别举例说明

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。

二，NLTK、Stanford NLP中对命名实体识别的分类

NLTK和Stanford NLP中对命名实体识别的分类，如下图：在这里插入图片描述

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。

三，使用NLTK实现NER任务 1，示例文档

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.

2，python实现代码

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

四，使用Stanford NLP实现NER任务 1，使用Stanford NLP工具之前的环境安装 1）安装Java

在使用Stanford NLP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。在ubuntu系统中下载java8的方法如下：https://blog.csdn.net/mucaoyx/article/details/82949450 Javal所在路径如下：/opt/java/jdk1.8.0_261/bin

2）下载Stanford NER

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 在这里插入图片描述下载Stanford NER的zip文件解压后的文件夹的路径为：/home/nijiahui/anaconda3/envs/nlp/lib/python3.6/site-packages/stanford-ner-4.0.0，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 2，Python实现Stanford NER代码

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

五，总结

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，语料，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。

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