NLP（一）语料库和WordNet

原文链接：http://www.one2know.cn/nlp1/

• 访问语料库
  NLTK数据库的安装：http://www.nltk.org/data.html
  NLTK语料库列表：http://www.nltk.org/nltk_data/
  内部访问(以Reuters corpus为例)：

import nltk
from nltk.corpus import reuters

# 下载路透社语料库
nltk.download('reuters')

# 查看语料库的内容
files = reuters.fileids()
print(files)

# 访问其中一个文件的内容
words14826 = reuters.words(['test/14826'])
print(words14826[:20])

# 输出主题（一共90个）
reutersGenres = reuters.categories()
print(reutersGenres)

# 访问一个主题，一句话一行输出
for w in reuters.words(categories=['tea']):
    print(w + ' ',end='')
    if w is '.':
        print()

• 下载外部语料库并访问（以影评数据集为例）
  下载数据集：http://www.cs.cornell.edu/people/pabo/movie-review-data/
  本例下载了1000积极和1000消极的影评

from nltk.corpus import CategorizedPlaintextCorpusReader

# 读取语料库
reader = CategorizedPlaintextCorpusReader(r'D:PyCharm 5.0.3WorkSpace2.NLP语料库1.movie_review_data_1000	xt_sentoken',r'.*.txt',cat_pattern=r'(w+)/*')
print(reader.categories())
print(reader.fileids())

# 语料库分成两类
posFiles = reader.fileids(categories='pos')
negFiles = reader.fileids(categories='neg')

# 从posFiles或negFiles随机选择一个文件
from random import randint
fileP = posFiles[randint(0,len(posFiles)-1)]
fileN = negFiles[randint(0,len(negFiles)-1)]

# 逐句打印随机的选择文件
for w in reader.words(fileP):
    print(w + ' ',end='')
    if w is '.':
        print()
for w in reader.words(fileN):
    print(w + ' ',end='')
    if w is '.':
        print()

CategorizedPlaintextCorpusReader类通过参数的设置，从内部将样本加载到合适的位置

• 语料库中的词频计算和计数分布分析
  以布朗语料库为例：布朗大学 500个文本 15个类

import nltk
from nltk.corpus import brown

nltk.download('brown')

# 查看brown中的类别
print(brown.categories())

# 挑选出三种类别，并获取其中的疑问词
genres = ['fiction','humor','romance']
whwords = ['what','which','how','why','when','where','who']

# 迭代器分别分析3种类
for i in range(0,len(genres)):
    genre = genres[i]
    print()
    print("Analysing '"+ genre + "' wh words")
    genre_text = brown.words(categories = genre)
    print(genre_text)

    # 返回输入单词对象的wh类及对应的频率
    fdist = nltk.FreqDist(genre_text)
    for wh in whwords:
        print(wh + ':',fdist[wh],end='  ')
    print()

输出：

['adventure', 'belles_lettres', 'editorial', 'fiction', 'government', 'hobbies', 'humor', 'learned', 'lore', 'mystery', 'news', 'religion', 'reviews', 'romance', 'science_fiction']

Analysing 'fiction' wh words
['Thirty-three', 'Scotty', 'did', 'not', 'go', 'back', ...]
what: 128  which: 123  how: 54  why: 18  when: 133  where: 76  who: 103  

Analysing 'humor' wh words
['It', 'was', 'among', 'these', 'that', 'Hinkle', ...]
what: 36  which: 62  how: 18  why: 9  when: 52  where: 15  who: 48  

Analysing 'romance' wh words
['They', 'neither', 'liked', 'nor', 'disliked', 'the', ...]
what: 121  which: 104  how: 60  why: 34  when: 126  where: 54  who: 89  

• 网络文本和聊天文本的词频分布

import nltk
from nltk.corpus import webtext

# nltk.download('webtext')
print(webtext.fileids())

# 选择一个数据文件，并计算频率分布,获得FreqDist的对象fdist
fileid = 'singles.txt'  # 个人广告
wbt_words = webtext.words(fileid)
fdist = nltk.FreqDist(wbt_words)

# 获取高频单词及其计数
print('最多出现的词 "' , fdist.max() , '" :' , fdist[fdist.max()])

# 获取所有单词的计数
print(fdist.N())

# 找出最常见的10个词
print(fdist.most_common(10))

# 将单词和频率制成表格
print(fdist.tabulate(5))

# 将单词和频率制成分布图
fdist.plot(cumulative=True) # 计数显示，cumulative=percents为百分比显示

输出：

['firefox.txt', 'grail.txt', 'overheard.txt', 'pirates.txt', 'singles.txt', 'wine.txt']
最多出现的词 " , " : 539
4867
[(',', 539), ('.', 353), ('/', 110), ('for', 99), ('and', 74), ('to', 74), ('lady', 68), ('-', 66), ('seeks', 60), ('a', 52)]
  ,   .   / for and 
539 353 110  99  74 
None

累计计数分布图：

• 使用WordNet获取一个词的不同含义

# import nltk
# nltk.download('wordnet')

from nltk.corpus import wordnet as wn
chair = 'chair'

# 输出chair的各种含义
chair_synsets = wn.synsets(chair)
print('Chair的意思:',chair_synsets,'

')

# 迭代输出 含义，含义的定义，同义词条，例句
for synset in chair_synsets:
    print(synset,': ')
    print('Definition: ',synset.definition())
    print('Lemmas/Synonymous words: ',synset.lemma_names())
    print('Example: ',synset.examples(),'
')

输出：

Chair的意思: [Synset('chair.n.01'), Synset('professorship.n.01'), Synset('president.n.04'), Synset('electric_chair.n.01'), Synset('chair.n.05'), Synset('chair.v.01'), Synset('moderate.v.01')] 

Synset('chair.n.01') : 
Definition:  a seat for one person, with a support for the back
Lemmas/Synonymous words:  ['chair']
Example:  ['he put his coat over the back of the chair and sat down'] 

Synset('professorship.n.01') : 
Definition:  the position of professor
Lemmas/Synonymous words:  ['professorship', 'chair']
Example:  ['he was awarded an endowed chair in economics'] 

Synset('president.n.04') : 
Definition:  the officer who presides at the meetings of an organization
Lemmas/Synonymous words:  ['president', 'chairman', 'chairwoman', 'chair', 'chairperson']
Example:  ['address your remarks to the chairperson'] 

Synset('electric_chair.n.01') : 
Definition:  an instrument of execution by electrocution; resembles an ordinary seat for one person
Lemmas/Synonymous words:  ['electric_chair', 'chair', 'death_chair', 'hot_seat']
Example:  ['the murderer was sentenced to die in the chair'] 

Synset('chair.n.05') : 
Definition:  a particular seat in an orchestra
Lemmas/Synonymous words:  ['chair']
Example:  ['he is second chair violin'] 

Synset('chair.v.01') : 
Definition:  act or preside as chair, as of an academic department in a university
Lemmas/Synonymous words:  ['chair', 'chairman']
Example:  ['She chaired the department for many years'] 

Synset('moderate.v.01') : 
Definition:  preside over
Lemmas/Synonymous words:  ['moderate', 'chair', 'lead']
Example:  ['John moderated the discussion'] 

• 上位词和下位词
  下位词更具体，上位词更一般（泛化）
  以bed.n.01和woman.n.01为例：

from nltk.corpus import wordnet as wn

woman = wn.synset('woman.n.01')
bed = wn.synset('bed.n.01')

# 返回据有直系关系的同义词集，上位词！
print(woman.hypernyms())
woman_paths = woman.hypernym_paths()

# 打印从根节点到woman.n.01的所有路径
for idx,path in enumerate(woman_paths):
    print('

Hypernym Path :',idx+1)
    for synset in path:
        print(synset.name(),',',end='')

# 更具体的术语，下位词！
types_of_bed = bed.hyponyms()
print('

Types of beds(Hyponyms): ',types_of_bed)

# 打印出更有意义的lemma(词条)
print('
',sorted(set(lemma.name() for synset in types_of_bed for lemma in synset.lemmas())))

输出：

[Synset('adult.n.01'), Synset('female.n.02')]

Hypernym Path : 1
entity.n.01 ,physical_entity.n.01 ,causal_agent.n.01 ,person.n.01 ,adult.n.01 ,woman.n.01 ,

Hypernym Path : 2
entity.n.01 ,physical_entity.n.01 ,object.n.01 ,whole.n.02 ,living_thing.n.01 ,organism.n.01 ,person.n.01 ,adult.n.01 ,woman.n.01 ,

Hypernym Path : 3
entity.n.01 ,physical_entity.n.01 ,causal_agent.n.01 ,person.n.01 ,female.n.02 ,woman.n.01 ,

Hypernym Path : 4
entity.n.01 ,physical_entity.n.01 ,object.n.01 ,whole.n.02 ,living_thing.n.01 ,organism.n.01 ,person.n.01 ,female.n.02 ,woman.n.01 ,

Types of beds(Hyponyms):  [Synset('berth.n.03'), Synset('built-in_bed.n.01'), Synset('bunk.n.03'), Synset('bunk_bed.n.01'), Synset('cot.n.03'), Synset('couch.n.03'), Synset('deathbed.n.02'), Synset('double_bed.n.01'), Synset('four-poster.n.01'), Synset('hammock.n.02'), Synset('marriage_bed.n.01'), Synset('murphy_bed.n.01'), Synset('plank-bed.n.01'), Synset('platform_bed.n.01'), Synset('sickbed.n.01'), Synset('single_bed.n.01'), Synset('sleigh_bed.n.01'), Synset('trundle_bed.n.01'), Synset('twin_bed.n.01'), Synset('water_bed.n.01')]

 ['Murphy_bed', 'berth', 'built-in_bed', 'built_in_bed', 'bunk', 'bunk_bed', 'camp_bed', 'cot', 'couch', 'deathbed', 'double_bed', 'four-poster', 'hammock', 'marriage_bed', 'plank-bed', 'platform_bed', 'sack', 'sickbed', 'single_bed', 'sleigh_bed', 'truckle', 'truckle_bed', 'trundle', 'trundle_bed', 'twin_bed', 'water_bed']

• 基于WordNet计算某种词性的多义性
  以名词n为例：

from nltk.corpus import wordnet as wn

type = 'n' #动词v，副词r，形容词a

# 返回WordNet中所有type类型的同义词集
sysnets = wn.all_synsets(type)

# 将所有词条合并成一个大list
lemmas = []
for sysnet in sysnets:
    for lemma in sysnet.lemmas():
        lemmas.append(lemma.name())

# 删除重复词条,list=>set
lemmas = set(lemmas)

# 计算每个词条type类型的含义数并加到一起
count = 0
for lemma in lemmas:
    count = count + len(wn.synsets(lemma,type)) # lemma在type类型下的所有含义

# 打印所有数值
print('%s总词条数: '%(type),len(lemmas))
print('%s总含义数: '%(type),count)
print('%s平均多义性: '%(type),count/len(lemmas))

输出：

n总词条数:  119034
n总含义数:  152763
n平均多义性:  1.2833560159282222