Python NLP

• . ：代替任何单个字符
• ^a ：匹配所有以字母a开头的字符串
• a& ：匹配所有以字母a结尾的字符串
• r"\\" ：匹配反斜杠
• [0-9]：任选一个
• [0-9]{3}：[0-9]重复三次

#return true or false
re.search(regex, string);
#return [] exist in string;
re.selectall(regex, string);

将空值替换为“0”的操作：

import numpy as np
matrix = np.genfromtxt("....csv", dtype = 'U75', skip_header = 1, delimiter = ',')
for i in range(np.shape[0]):
    column = (matrix[:, i] == '')
    matrix[column, i] = '0'

#数据转换
vector = vector.astype(float)

NLTK

import nltk
nltk.download('gutenberg')

Linux平台

Ubuntu 自带python2和python3 设置默认Python版本和切换：

直接执行这两个命令即可：
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 100
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 150
如果要切换到Python2，执行：
sudo update-alternatives --config python
按照提示输入选择数字回车即可。

由于Ubuntu自带的pip最高版本达不到下载nltk的要求：（强制重装pip）

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
sudo    python get-pip.py --force-reinstall

然后再

pip install nltk

配置Java环境

gedit出错：gedit是用户图形界面，在服务器上打不开...所以无法进入gedit编辑器

此时直接修改的配置文件：

JAVA_HOME=/usr/lib/jvm/java1.8
JRE_HOME=/usr/lib/jvm/java1.8/jre
PATH=$JAVA_HOME/bin:$PATH
CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export JAVA_HOME
export JRE_HOME
export PATH

添加相同的代码就可以，最后java -version（一个-），就成功了。

linux nltk包的位置：/usr/local/lib/python3.6/dist-packages

NLTK Stanford NLP

Python NLTK结合Stanford NLP工具包进行分词、词性标注、句法分析

在NLTK中使用stanford NLP工具包

一些不清楚的地方：

比如说文中说的的stanfordNLTK目录在哪？

作者的资源链接应该标错了，估摸着最近应该得不到回复，我一个一个下载，整理一下：

NLP

一、文本处理

1.1 分句

import nltk
from nltk.corpus import gutenberg
from pprint import pprint
import numpy as np

alice = gutenberg.raw(fileids='carroll-alice.txt')
default_st = nltk.sent_tokenize
 
print('\nTotal sentences in alice:', len(alice_sentences))
print('First 5 sentences in alice:-')
print(np.array(alice_sentences[0:5]))

punkt_st = nltk.tokenize.PunktSentenceTokenizer()
sample_sentences = punkt_st.tokenize(sample_text)
print(np.array(sample_sentences))

正则表达式：

SENTENCE_TOKENS_PATTERN = r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?|\!)\s'
regex_st = nltk.tokenize.RegexpTokenizer(
            pattern=SENTENCE_TOKENS_PATTERN,
            gaps=True)
sample_sentences = regex_st.tokenize(sample_text)
print(np.array(sample_sentences))

1.2 分词

默认：

default_wt = nltk.word_tokenize
words = default_wt(sample_text)
np.array(words)

Treebank

treebank_wt = nltk.TreebankWordTokenizer()
words = treebank_wt.tokenize(sample_text)
np.array(words)

正则表达式：r'\w+'

GAP_PATTERN = r'\s+'        
regex_wt = nltk.RegexpTokenizer(pattern=GAP_PATTERN,
                                gaps=True)
words = regex_wt.tokenize(sample_text)
np.array(words)
word_indices = list(regex_wt.span_tokenize(sample_text))
print(word_indices)#输出分割后字符在原始的位置
print(np.array([sample_text[start:end] for start, end in word_indices]))
#输出每个字符

 

def tokenize_text(text):
    sentences = nltk.sent_tokenize(text)
    word_tokens = [nltk.word_tokenize(sentence) for sentence in sentences] 
    return word_tokens
 
sents = tokenize_text(sample_text)
np.array(sents)
 
words = [word for sentence in sents for word in sentence]
np.array(words)

更快的分词分句:

import spacy
nlp = spacy.load('en_core', parse = True, tag=True, entity=True)
 
text_spacy = nlp(sample_text)
sents = np.array(list(text_spacy.sents))
sent_words = [[word.text for word in sent] for sent in sents]
np.array(sent_words)
words = [word.text for word in text_spacy]
np.array(words)

去除重音字符：

import unicodedata
 
def remove_accented_chars(text):
    text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')
    return text
 
remove_accented_chars('Sómě Áccěntěd těxt')

删除特殊字符：

def remove_special_characters(text, remove_digits=False):
    pattern = r'[^a-zA-z0-9\s]' if not remove_digits else r'[^a-zA-z\s]'
    text = re.sub(pattern, '', text)
    return text
 
remove_special_characters("Well this was fun! What do you think? 123#@!", 
                          remove_digits=True)

拓展略缩词：

from contractions import CONTRACTION_MAP
import re
 
def expand_contractions(text, contraction_mapping=CONTRACTION_MAP):
    
    contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), 
                                      flags=re.IGNORECASE|re.DOTALL)
    def expand_match(contraction):
        match = contraction.group(0)
        first_char = match[0]
        expanded_contraction = contraction_mapping.get(match)\
                                if contraction_mapping.get(match)\
                                else contraction_mapping.get(match.lower())                       
        expanded_contraction = first_char+expanded_contraction[1:]
        return expanded_contraction
        
    expanded_text = contractions_pattern.sub(expand_match, text)
    expanded_text = re.sub("'", "", expanded_text)
    return expanded_text

缩略词列表：链接：https://pan.baidu.com/s/1qu44acyb6pwMuUtfBqimig  提取码：5rnf