from nltk.tokenize import sent_tokenize ,word_tokenize
sentence = 'jainmiah I love you but you are not bothering about my request,
please yaar consider me for the sake'
word_tok = word_tokenize(sentence)
print(word_tok)
set_all = set(word_tokenize(sentence))
print(set_all)
实际
我有一些字符串,我想把它们解析成一个“块”列表。我的字符串看起来像这样
"some text [[anchor]] some more text, [[another anchor]]. An isolated ["
我希望能得到像这样的东西
[
TextChunk "some text ",
Anchor "anchor",
TextChunk " some more text, "
Anchor "another anchor",
TextChunk ". An isola
我有以下python代码:
text = "this’s a sent tokenize test. this is sent two. is this sent three? sent 4 is cool! Now it’s your turn."
from nltk.tokenize import sent_tokenize
sent_tokenize_list = sent_tokenize(text)
import numpy as np
lenDoc=len(sent_tokenize_list)
features={'position',
我在mac 10.11.4中使用python空闲3.5.1,我在python中直接执行以下代码,它工作得很好
>>> import nltk
>>> from nltk.tokenize import sent_tokenize, word_tokenize
>>> sample_sentence = "Hi, this is a sample sentence. Python is great"
>>> sample_sentence
'Hi, this is a sample sentence.
我有跟踪错误。
if form in exceptions: TypeError: unhashable type: 'list'
以下是我的密码。
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
sentence = 'missed you'
w_tokenize = (word_tokenize(sentence))
for word in w_tokenize:
print WordNetLemmatizer().lem
from nltk.tokenize import word_tokenize
music_comments = [['So cant you just run the bot outside of the US? ', ''], ["Just because it's illegal doesn't mean it will stop. I hope it actually gets enforced. ", ''], ['Can they do something about all the fuck
当我尝试使用TypeError从PDF中提取文本时,我得到了一个:“不能在类似字节的对象上使用字符串模式”。有谁能帮忙解决这个问题吗?当我打印(文本)时,我从我想要提取的PDF中获取文本,尽管格式有点奇怪。然而,文本等只包含数字.?
import textract
import os
from nltk.tokenize import word_tokenize
for filename in os.listdir('Harbour PDF'):
if '.DS_Store' == filename:
continue
filename = '
在德语文本中使用sent_tokenizer时,我会有一些奇怪的行为。
示例代码:
sent_tokenizer = nltk.data.load('tokenizers/punkt/german.pickle')
for sent in sent_tokenizer.tokenize("Super Qualität. Tolles Teil.")
print sent
这与错误失败:
Traceback (most recent call last):
for sent in sent_tokenize("Super Qualität. T
下面的代码按预期编译和运行:
fun {Tokenize Lexemes}
case Lexemes of
Head|Tail then
case Head of
"+" then
operator(type:plus)|{Tokenize Tail}
else
if {String.isFloat Head} then
number(Head)|{Tokenize Tai
我是新来的。我在试一些基本的东西。
import nltk
nltk.word_tokenize("Tokenize me")
给出以下错误
Traceback (most recent call last):
File "<pyshell#27>", line 1, in <module>
nltk.word_tokenize("hi im no onee")
File "C:\Python27\lib\site-packages\nltk\tokenize\__init__.py", line 101,
两台机器,都运行Ubuntu14.04.1。相同的源代码运行在相同的数据上。一个很好,一个抛出编解码器0xe2错误。为什么会这样呢?(更重要的是,我如何修复它?)
违规代码似乎是:
def tokenize(self):
"""Tokenizes text using NLTK's tokenizer, starting with sentence tokenizing"""
tokenized=''
for sentence in sent_tokenize(self):
toke
我试图为我用Rust写的一个项目写文档。其中一个文档需要使用regex::Regex。这是我想写的医生:
/// Return a list of the offsets of the tokens in `s`, as a sequence of `(start, end)`
/// tuples, by splitting the string at each successive match of `regex`.
///
/// # Examples
///
/// ```
/// use rusty_nltk::tokenize::util::regexp_span_tokenize
我正在尝试清理电子表格中的文本数据,但它没有NAs。我面对这个错误:TypeError: expected string or bytes-like object。 import nltk
import numpy as np
import pandas as pd
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
paragraph=pd.read_excel("..")
paragraph.info()
我想使用ElementType.PARAMETER注释运行一个方面,但它不起作用。从来不调用@Around tokenize方法。
@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.PARAMETER)
public @interface Tokenize {}
@Aspect
@Component
public class TokenizeAspect {
@Around("@annotation(Tokenize)")
public Object tokenize(ProceedingJoinPo
我想把西班牙语句子翻译成单词。以下是正确的方法,还是有更好的方法?
import nltk
from nltk.tokenize import word_tokenize
def spanish_word_tokenize(s):
for w in word_tokenize(s):
if w[0] in ("¿","¡"):
yield w[0]
yield w[1:]
else:
yield w
sentenc
我用OCaml编写了一个用于基本算术表达式的尾部递归扫描器
语法
Exp ::= n | Exp Op Exp | (Exp)
Op ::= + | - | * | /
type token =
| Tkn_NUM of int
| Tkn_OP of string
| Tkn_LPAR
| Tkn_RPAR
| Tkn_END
exception ParseError of string * string
let tail_tokenize s =
let rec tokenize_rec s pos lt =
if pos < 0 then lt
else
我正在做情绪分析。初始代码只针对一个字符串。但是,我想让程序运行并处理.csv文件中每个句子的情感分析。该程序使用vs代码运行。 下面是我是如何修改代码的: fp = open('C:/Users/User/Desktop/hj.txt',encoding='utf-8' ,errors='ignore' ) # Open file on read mode
lines = fp.read().split("\n") # Create a list containing all lines
fp.close()
尝试使用word_tokenize和sent_tokenize标记带有新行的段落,但它无法识别新行。
尝试通过将其拆分到新行中来将其分成段落,但仍然不起作用。
from nltk import sent_tokenize, word_tokenize, pos_tag
para="the new line \n new char"
sent=sent_tokenize(para)
print(sent)
输出:
['the new line \n new char']
如果在python中以字符串格式指定数据,但在从docx文件中提取数据时失败,则它可以工作
对
我曾尝试将sys.path.append()与os.getcwd()结合使用,但它没有起作用。
该源来自,我下载并解压缩它们如下:
alvas@ubi:~/test$ wget https://github.com/alvations/DLTK/archive/master.zip
alvas@ubi:~/test$ tar xvzf master.zip
alvas@ubi:~/test$ cd DLTK-master/; ls
dltk
alvas@ubi:~/test/DLTK-master$ cd dltk/; ls
tokenize
alvas@ubi:~/test/DLTK-m
我正在尝试创建我自己的标记语料库,在去角色化的数据集上,数据集有大约6250条tweet。代码如下所示,尽管它给出了大小为200项的小型数据集的结果。
df = pd.read_csv('Demonetization_data29th2.csv',encoding = "ISO-8859-1")
text = df['CONTENT']
sentiment = df['sentiment']
a =[]
tagged = [[nltk.word_tokenize(sent)] for sent in df['CONTE