《Python自然语言处理》第二章 学习笔记

import nltk
from nltk.book import *

nltk.corpus.gutenberg.fileids()

emma = nltk.corpus.gutenberg.words('austen-emma.txt')
len(emma)

emma = nltk.Text(nltk.corpus.gutenberg.words('austen-emma.txt'))
emma.concordance("surprize")

from nltk.corpus import gutenberg
gutenberg.fileids()
emma = gutenberg.words('austen-emma.txt')

for fileid in gutenberg.fileids():
    num_char = len(gutenberg.raw(fileid))
    num_words = len(gutenberg.words(fileid))
    num_sents = len(gutenberg.sents(fileid))
    num_vocab = len(set([w.lower() for w in gutenberg.words(fileid)]))
    print int(num_char / num_words), int(num_words / num_sents), int(num_words / num_vocab), fileid

macbeth_sentences = gutenberg.sents('shakespeare-macbeth.txt')  # 将文本划分为句子
macbeth_sentences[1037]
longest_len = max([len(s) for s in macbeth_sentences])  # 获得最长的句子
[s for s in macbeth_sentences if len(s) == longest_len]

from nltk.corpus import webtext
for fileid in webtext.fileids():
    print fileid, webtext.raw(fileid)[:65], '...'

    from nltk.corpus import nps_chat
    chatroom = nps_chat.posts('10-19-20s_706posts.xml')
    chatroom[123]

    from nltk.corpus import brown
brown.categories()  # 得到文本的各分类类别
brown.words(categories='news')  # 指定特定的类别或文件阅读
brown.words(fileids=['cg22'])
brown.sents(categories=['news', 'editorial', 'reviews'])

from nltk.corpus import brown
news_text = brown.words(categories='news')  # 在新闻文体中的词s
fdist = FreqDist([w.lower() for w in news_text])  # 化为字典形式，并略掉大小写
modals = ['can', 'could', 'may', 'might', 'must', 'will']
for m in modals:
    print m + ':', fdist[m],

    from nltk.corpus import brown
    import nltk
    cfd = nltk.ConditionalFreqDist((genre, word) for genre in brown.categories(
    ) for word in brown.words(categories=genre))
    genres = ['news', 'religion', 'hobbies',
              'science_fiction', 'romance', 'humor']
    modals = ['can', 'could', 'may', 'might', 'must', 'will']
    cfd.tabulate(conditions=genres, samples=modals)

    from nltk.corpus import reuters
reuters.fileids()  # 测试文档
reuters.categories()  # 路透社语料库的类别

# 查找由一个或多个文档涵盖的主题，也可以查找包含在一个或多个类别中的文档。语料库方法既接受单个的fileid也接受fileids列表作为参数
reuters.categories('traing/9865')
reuters.categories(['traing/9865', 'traing/9880'])

reuters.fileids('barley')
reuters.fileids(['barley', 'corn'])
# 可以以文档或类别为单位查找我们想要的词或句子
reuters.words('traing/9865')[:14]
reuters.words(['traing/9865', 'traing/9880'])

reuters.words(categories='barley')
reuters.words(categories=['barley', 'corn'])

from nltk.corpus import inaugural
inaugural.fileids()
[fileid[:4] for fileid in inaugural.fileids()]

cfd = nltk.ConditionalFreqDist((target, fileid[:4])
                               for fileid in inaugural.fileids()
                               for w in inaugural.words(fileid)
                               for target in ['america', 'citizen']
                               if w.lower().startswith(target))
cfd.plot()

nltk.corpus.cess_esp.words()
nltk.corpus.floresta.words()
nltk.corpus.indian.words('hindi.pos')
nltk.corpus.udhr.fileids()
nltk.corpus.udhr.words('Javanese-Latin1')[11:]

from nltk.corpus import udhr
languages = ['Chickasaw', 'English', 'German_Deutsch',
             'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']
cfd = nltk.ConditionalFreqDist(
    (lang, len(word)) for lang in languages for word in udhr.words(lang + '-Latin1'))
cfd.plot(cumulative=True)

raw = gutenberg.raw("burgess-busterbrown.txt")
raw[1:20]
words = gutenberg.words("burgess-busterbrown.txt")
words[1:20]
sents = gutenberg.sents("burgess-busterbrown.txt")
sents[1:20]

# #http://blog.youkuaiyun.com/shanyuelanhua/article/details/51212194
# from nltk.corpus import BracketParseCorpusReader
# corpus_root =r"F:\nltk_data\corpora\SogouC.reduced.20061127\SogouC.reduced\Reduced"  # r"" 防止转义
# file_pattern = r".*/.*\.txt"    #匹配corpus_root目录下的所有子目录下的txt文件
# ptb = BracketParseCorpusReader(corpus_root, file_pattern)   #初始化读取器：语料库目录和要加载文件的格式，默认utf8格式的编码
# ptb.fileids()   #至此，可以看到目录下的所有文件名，例如C000008/1001.txt，则成功了
# ptb.raw(“C000008/1001.txt”) # 如果C000008/1001.txt编码格式和ptb格式一致，则看到内容

# from nltk.corpus import PlaintextCorpusReader
# corpus_root = r"F:\nltk_data\corpora\SogouC.reduced.20061127\SogouC.reduced\Reduced"
# file_pattern = r"1001\.txt"
# wordlists = PlaintextCorpusReader(corpus_root, file_pattern)
# wordlists.fileids()
# wordlists.words("1001.txt")

from nltk.corpus import BracketParseCorpusReader
corpus_root = r"C:\Users\Tony\AppData\Roaming\nltk_data\SogouC.reduced\Reduced"  # r"" 防止转义
file_pattern = r".*/.*\.txt"  # 匹配corpus_root目录下的所有子目录下的txt文件
# 初始化读取器：语料库目录和要加载文件的格式，默认utf8格式的编码
ptb = BracketParseCorpusReader(corpus_root, file_pattern)
ptb.fileids()  # 至此，可以看到目录下的所有文件名，例如C000008/1001.txt，则成功了
ptb.raw(“C000008 / 1001.txt”)  # 如果C000008/1001.txt编码格式和ptb格式一致，则看到内容

from nltk.corpus import PlaintextCorpusReader
corpus_root = r"C:\Users\Tony\AppData\Roaming\nltk_data\SogouC.reduced\Reduced"
file_pattern = r"1001\.txt"
wordlists = PlaintextCorpusReader(corpus_root, file_pattern)
wordlists.fileids()
wordlists.words("1001.txt")

from nltk.corpus import brown
cfd = nltk.ConditionalFreqDist(
    (genre, word)
    for genre in brown.categories()
    for word in brown.words(categories=genre))

genre_word = [
    (genre, word)
    for genre in ['news', 'romance']
    for word in brown.words(categories=genre)]
len(genre_word)

genre_word[:4]
genre_word[-4:]

cfd = nltk.ConditionalFreqDist(genre_word)
cfd
cfd.conditions()

cfd['news']
cfd['romance']
list(cfd['romance'])
cfd['romance']['could']

from nltk.corpus import inaugural
cfd = nltk.ConditionalFreqDist(
    (target, fileid[:4])
    for fileid in inaugural.fileids()
    for w in inaugural.words(fileid)
    for target in ['america', 'citizen']
    if w.lower().startswith(target))

from nltk.corpus import udhr
languages = ['Chickasaw', 'English', 'German_Deutsch',
             'Greenlandic_Inuktikut', 'Hungarian_Magyar', 'Ibibio_Efik']
cfd = nltk.ConditionalFreqDist(
    (lang, len(word))
    for lang in languages
    for word in udhr.words(lanng + '-Latin1'))

cfd.tabulate(
    conditions=['English', 'German_Deutsch'],
    samples=range(10),
    cumulative=True)

sent = ['In', 'the', 'beginning', 'God', 'created',
        'the', 'heaven', 'and', 'the', 'earth', '.']
nltk.bigrams(sent)


def generate_model(cfdist, word, num=15):
    for i in range(num):
        print word,
        word = cfdist[word].max()

        text = nltk.corpus.genesis.words('english-kjv.txt')
        bigrams = nltk.bigrams(text)
        cfd = nltk.ConditionalFreqDist(bigrams)

        print cfd['living']
        generate_model(cfd, 'living')

# 模块的使用
import sys
sys.path.append(
    r'C:\Users\Tony\Documents\Workspace\Python\NLP with Python\Chapter 2')
form textproc import *
plural("fairy")
plural("woman")
# textproc.py


def plural(word):
    if word.endswith('y'):
        return word[:-1] + 'ies'
    elif word[-1] in 'sx' or word[-2:] in ['sh', 'ch']:
        return word + 'es'
    elif word.endswith('an'):
        return word[:-2] + 'en'
    else:
        return word + 's'

# 计算文本的词汇表，删除所有在现有的词汇列表出现的元素，只留下罕见的或拼写错误的词汇


def unusual_words(text):
    text_vocab = set(w.lower() for w in text if w.isalpha())
    english_vocab = set(w.lower() for w in nltk.corpus.words.words())
    unusual = text_vocab.difference(english_vocab)
    return sorted(unusual)

    unusual_words(nltk.corpus.gutenberg.words('austen-sense.txt'))
    unusual_words(nltk.corpus.nps_chat.words())

    from nltk.corpus import stopwords
    stopwords.words('english')

# 计算文本中不包含在停用词列表中的词所占的比例


def content_fraction(text):
    stopwords = nltk.corpus.stopwords.words('english')
    content = [w for w in text if w.lower() not in stopwords]
    return len(content) / len(text)

    content_fraction(nltk.corpus.reuters.words())

# 词谜
puzzle_letters = nltk.FreqDist('egivrvonl')
obligatory = 'r'
wordlist = nltk.corpus.words.words()
[w for w in wordlist if len(w) >= 6
 and obligatory in w
    and nltk.FreqDist(w) <= puzzle_letters]  # 利用FreqDist比较法检查候选词中的每个字母出现的频率是否小于或等于其相应在词谜中出现的概率

# 同时出现在男性和女性文件中的名字
names = nltk.corpus.names
names.fileids()
male_names = names.words('male.txt')
female_names = names.words('female.txt')
[w for w in male_names if w in female_names]
# 男性和女性名字的结尾字母
cfd = nltk.ConditionalFreqDist(
    (fileids, name[-1])
    for fileids in names.fileids()
    for name in names.words(fileids))

cfd.plot()

# 发音的词典
entries = nltk.corpus.cmudict.entries()
len(entries)
for entry in entries[39943:39951]:
    print entry

# 扫描词典中发音包含三个音素的条目
for word, pron in entries:
    if len(pron) == 3:
        ph1, ph2, ph3 = pron
        if ph1 == 'P' and ph3 == 'T':
            print word, ph2,

            syllable = ['N', 'IHO', 'K', 'S']
[word for word, pron in entries if pron[-4:] == syllable]  # ?

# 以'n'结尾并发'M'的音的词汇
[w for w, pron in entries if pron[-1] == 'M' and w[-1] == 'n']
# 以'n'开头并发'N'的音的词汇
sorted(set(w[:2] for w, pron in entries if pron[0] == 'N' and w[0] != 'n'))

# 主重音(1)、次重音(2)、无重音(0)
# 提取重音数字


def stress(pron):
    return [char for phone in pron for char in phone if char.isdigit()]
# 扫描字典，找到特定重音模式的词汇
[w for w, pron in entries if stress(pron) == ['0', '1', '0', '2', '0']]
[w for w, pron in entries if stress(pron) == ['0', '2', '0', '1', '0']]

# 使用条件频率分布寻找相应词汇的最小对比集
p3 = [(pron[0] + '-' + pron[2], word)  # 按照三音素词的第一个和最后一个音素来分组
      for (word, pron) in entries
      if pron[0] == 'P' and len(pron) == 3]  # 找到所哟p开头的三音素词
    cfd = nltk.ConditionalFreqDist(p3)
    for template in cfd.conditions():
        if len(cfd[template]) > 10:
            words = cfd[template].keys()
            wordlist = ' '.join(words)
            print template, wordlist[:70] + "..."

# 通过特定词汇来访问它
prondict = nltk.corpus.cmudict.dict()
prondict['fire']  # 通过指定词典的名字及后面带方括号的关键字来查词典
prondict['blog']  # 如果试图查找一个不存在的关键字，就会得到一个KeyError
prondict['blog'] = [['B', 'L', 'AAl', 'G']]
prondict['blog']

# 文本到发音
text = ['natural', 'language', 'processing']
[ph for w in text for ph in prondict[w][0]]

# 比较词表
from nltk.corpus import swadesh
swadesh.fileids()
swadesh.words('en')
# 通过使用entries()方法来指定一个语言链表来访问多语言中的同源词
fr2en = swadesh.entries(['fr', 'en'])
fr2en
translate = dict(fr2en)
translate['chien']
translate['jeter']
# 使用dict()函数把德语-英语和西班牙语-英语对相互转换成一个词典，然后用这些添加的映射更新原有的translate词典
de2en = swadesh.entries(['de', 'en'])  # German -> English
es2en = swadesh.entries(['es', 'en'])  # Spanish -> English
translate.update(dict(de2en))
translate.update(dict(es2en))
translate['Hund']
translate['perro']
# 比较德语族和拉丁语族的不同
languages = ['en', 'de', 'nl', 'es', 'fr', 'pt', 'la']
for i in [139, 140, 141, 142]:
    print swadesh.entries(languages)[i]

# 词汇工具 Toolbox和Shoebox
from nltk.corpus import toolbox
toolbox.entries('rotokas.dic')

# WordNet
# 意义与同义词
from nltk.corpus import wordnet as wn
wn.synsets('motorcar')  # 定义同义词集
wn.synset('car.n.01').lemma_names()  # 访问同义词集
wn.synset('car.n.01').definition()  # 同义词集定义
wn.synset('car.n.01').examples()  # 同义词集例句
wn.synset('car.n.01').lemmas()  # 同义词集的所有词条
wn.lemma('car.n.01.automobile')  # 查找特定的词条
wn.lemma('car.n.01.automobile').synset  # 得到一个词条所对应的同义词集
wn.lemma('car.n.01.automobile').name  # 得到一个词条的名字

wn.synsets('car')  # car 共有5个同义词集
for synset in wn.synsets('car'):
    print synset.lemma_names()
    print synset.definition()

    wn.synsets('dish')
    for synset in wn.synsets('dish'):
        print synset.definition()
# 下位词
motorcar = wn.synset('car.n.01')
types_of_motorcar = motorcar.hyponyms()
types_of_motorcar[26]
sorted([lemma.name for synset in types_of_motorcar for lemma in synset.lemmas()])
# 通过访问上位词来操纵层次结构。
motorcar.hypernyms()
paths = motorcar.hypernym_paths()
len(paths)
[synset.name for synset in paths[0]]
[synset.name for synset in paths[1]]
# 得到一个最笼统的上位(或根上位)同义词集
motorcar.root_hypernyms()
# 图形化WordNet浏览器
nltk.app.wordnet()

# 更多的词汇关系
wn.synset('tree.n.01').part_meronyms()  # 条目到部分
wn.synset('tree.n.01').substance_meronyms()  # 条目的组成
wn.synset('tree.n.01').member_holonyms()  # 条目的集合

for synset in wn.synsets('mint', wn.NOUN):
    print synset.name() + ':', synset.definition()

wn.synset('mint.n.04').part_holonyms()  # mint.n.04是mint.n.02的一部分，
wn.synset('mint.n.04').substance_holonyms()  # 同时也是组成mint.n.05的材料

wn.synset('walk.v.01').entailments()  # 走路蕴涵着抬脚
wn.synset('eat.v.01').entailments()
wn.synset('tease.v.03').entailments()

# 反义词
wn.lemma('supply.n.02.supply').antonyms()
wn.lemma('rush.v.01.rush').antonyms()
wn.lemma('horizontal.a.01.horizontal').antonyms()
wn.lemma('staccato.r.01.staccato').antonyms()

# 语义相似度
right = wn.synset('right_whale.n.01')
orca = wn.synset('orca.n.01')
minke = wn.synset('minke_whale.n.01')
tortoise = wn.synset('tortoise.n.01')
novel = wn.synset('novel.n.01')
right.lowest_common_hypernyms(minke)
right.lowest_common_hypernyms(orca)
right.lowest_common_hypernyms(tortoise)
right.lowest_common_hypernyms(novel)

# 通过查找每个同义词集的深度来量化这个普遍性的概念
wn.synset('baleen_whale.n.01').min_depth()
wn.synset('whale.n.02').min_depth()
wn.synset('vertebrate.n.01').min_depth()
wn.synset('entity.n.01').min_depth()

# 基于上位词层次概念中相互关联的最短路径下，在0~1范围内的相似度
right.path_similarity(minke)
right.path_similarity(orca)
right.path_similarity(tortoise)
right.path_similarity(novel)

# 帮助
help(wn)

# VerbNet
nltk.corpus.verbnet

 

转载于:https://www.cnblogs.com/yezuoxian/p/6709051.html