Question

我只是想知道NLTK在单词/句子标记化方面是否比正则表达更快。

Answer 1

默认nltk.word_tokenize()正在使用Treebank tokenizer来模仿Penn Treebank tokenizer中的标记生成器。

请注意str.split()在语言学意义上没有达到令牌，例如：

>>> sent = "This is a foo, bar sentence."
>>> sent.split()
['This', 'is', 'a', 'foo,', 'bar', 'sentence.']
>>> from nltk import word_tokenize
>>> word_tokenize(sent)
['This', 'is', 'a', 'foo', ',', 'bar', 'sentence', '.']

它通常用于分隔具有指定分隔符的字符串，例如在制表符分隔的文件中，您可以使用str.split('\t')，或者当您的文本文件每行有一个句子时，尝试使用换行符\n拆分字符串。

让我们在python3中做一些基准测试：

import time
from nltk import word_tokenize

import urllib.request
url = 'https://raw.githubusercontent.com/Simdiva/DSL-Task/master/data/DSLCC-v2.0/test/test.txt'
response = urllib.request.urlopen(url)
data = response.read().decode('utf8')

for _ in range(10):
    start = time.time()
    for line in data.split('\n'):
        line.split()
    print ('str.split():\t', time.time() - start)

for _ in range(10):
    start = time.time()
    for line in data.split('\n'):
        word_tokenize(line)
    print ('word_tokenize():\t', time.time() - start)

[OUT]：

str.split():     0.05451083183288574
str.split():     0.054320573806762695
str.split():     0.05368804931640625
str.split():     0.05416440963745117
str.split():     0.05299568176269531
str.split():     0.05304527282714844
str.split():     0.05356955528259277
str.split():     0.05473494529724121
str.split():     0.053118228912353516
str.split():     0.05236077308654785
word_tokenize():     4.056122779846191
word_tokenize():     4.052812337875366
word_tokenize():     4.042144775390625
word_tokenize():     4.101543664932251
word_tokenize():     4.213029146194458
word_tokenize():     4.411528587341309
word_tokenize():     4.162556886672974
word_tokenize():     4.225975036621094
word_tokenize():     4.22914719581604
word_tokenize():     4.203172445297241

如果我们从another tokenizers in bleeding edge NLTK尝试https://github.com/jonsafari/tok-tok/blob/master/tok-tok.pl：

import time
from nltk.tokenize import ToktokTokenizer

import urllib.request
url = 'https://raw.githubusercontent.com/Simdiva/DSL-Task/master/data/DSLCC-v2.0/test/test.txt'
response = urllib.request.urlopen(url)
data = response.read().decode('utf8')

toktok = ToktokTokenizer().tokenize

for _ in range(10):
    start = time.time()
    for line in data.split('\n'):
        toktok(line)
    print ('toktok:\t', time.time() - start)

[OUT]：

toktok:  1.5902607440948486
toktok:  1.5347232818603516
toktok:  1.4993178844451904
toktok:  1.5635688304901123
toktok:  1.5779635906219482
toktok:  1.8177132606506348
toktok:  1.4538452625274658
toktok:  1.5094449520111084
toktok:  1.4871931076049805
toktok:  1.4584410190582275

（注意：文本文件的来源是https://github.com/Simdiva/DSL-Task）

如果我们查看原生perl实施，则python的{{1}}与perl时间具有可比性。但是在python实现中这样做，在perl中预编译正则表达式，它不是the proof is still in the pudding：

ToktokTokenizer

（注意：在对alvas@ubi:~$ wget https://raw.githubusercontent.com/jonsafari/tok-tok/master/tok-tok.pl --2016-02-11 20:36:36-- https://raw.githubusercontent.com/jonsafari/tok-tok/master/tok-tok.pl Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.31.17.133 Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.31.17.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 2690 (2.6K) [text/plain] Saving to: ‘tok-tok.pl’ 100%[===============================================================================================================================>] 2,690 --.-K/s in 0s 2016-02-11 20:36:36 (259 MB/s) - ‘tok-tok.pl’ saved [2690/2690] alvas@ubi:~$ wget https://raw.githubusercontent.com/Simdiva/DSL-Task/master/data/DSLCC-v2.0/test/test.txt --2016-02-11 20:36:38-- https://raw.githubusercontent.com/Simdiva/DSL-Task/master/data/DSLCC-v2.0/test/test.txt Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.31.17.133 Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.31.17.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 3483550 (3.3M) [text/plain] Saving to: ‘test.txt’ 100%[===============================================================================================================================>] 3,483,550 363KB/s in 7.4s 2016-02-11 20:36:46 (459 KB/s) - ‘test.txt’ saved [3483550/3483550] alvas@ubi:~$ time perl tok-tok.pl < test.txt > /tmp/null real 0m1.703s user 0m1.693s sys 0m0.008s alvas@ubi:~$ time perl tok-tok.pl < test.txt > /tmp/null real 0m1.715s user 0m1.704s sys 0m0.008s alvas@ubi:~$ time perl tok-tok.pl < test.txt > /tmp/null real 0m1.700s user 0m1.686s sys 0m0.012s alvas@ubi:~$ time perl tok-tok.pl < test.txt > /tmp/null real 0m1.727s user 0m1.700s sys 0m0.024s alvas@ubi:~$ time perl tok-tok.pl < test.txt > /tmp/null real 0m1.734s user 0m1.724s sys 0m0.008s进行计时时，我们必须将输出传输到一个文件中，因此这里的时间包括机器输出到文件的时间，而在tok-tok.pl时间，它没有时间输出到文件中

关于nltk.tokenize.ToktokTokenizer，它有点不同，比较速度基准而不考虑准确性有点古怪。

考虑一下：

如果正则表达式将文本文件/段落分成1个句子，则速度几乎是瞬时的，即0完成工作。但那将是一个可怕的句子标记器......
如果文件中的句子已经由sent_tokenize()分隔，那么这只是比较\n与str.split('\n')和re.split('\n')之间没有任何关系的情况与句子标记化有关; P

有关nltk如何在NLTK中工作的信息，请参阅：

因此，为了有效地比较sent_tokenize()与其他基于正则表达式的方法（不是sent_tokenize()），还必须评估准确性，并以标记化格式生成具有人工评估句子的数据集。

考虑这项任务：https://www.hackerrank.com/challenges/from-paragraphs-to-sentences

鉴于文字：

在第三类中，他包括那些兄弟（大多数）共济会没有看到任何东西，只有外在的形式和仪式，以及珍惜这些形式的严格表现而不用担心他们的意图或意义。这就是Willarski，甚至是Grand 主要小屋的主人。最后，到第四类也是一个很多兄弟属于，特别是那些最近的兄弟加入。根据皮埃尔的观察，这些人是没有的人信仰任何东西，也不渴望任何东西，但加入了共济会只是为了与富有的年轻兄弟联系通过他们的联系或等级，以及有谁的影响力在小屋里很多人。皮埃尔开始对他的不满感到不满正在做。共济会，无论如何，他有时会在这里看到它在他看来，仅仅基于外部。他没有想到怀疑共济会本身，但怀疑俄罗斯砌体已采取了错误的道路并偏离其原始原则。等等他到国外去年底开始进入更高层订单的秘密。在这些情况下要做什么？至赞成革命，推翻一切，用武力击退武力？不！我们离那很远。每一次暴力改革都值得谴责在男人保持现状的同时也完全无法弥补邪恶因为智慧不需要暴力。＆＃34;但是有什么东西在跑步它是那样的吗？＆＃34;伊拉金的新郎说。＆＃34;一旦她错过了并且转过身来它离开了，任何杂种都可以接受它，＆＃34;伊拉金在说同样的话时间，从他的疾驰和他的兴奋中喘不过气来。

我们希望得到这个：

str.split('\n')

所以简单地做In the third category he included those Brothers (the majority) who saw nothing in Freemasonry but the external forms and ceremonies, and prized the strict performance of these forms without troubling about their purport or significance. Such were Willarski and even the Grand Master of the principal lodge. Finally, to the fourth category also a great many Brothers belonged, particularly those who had lately joined. These according to Pierre's observations were men who had no belief in anything, nor desire for anything, but joined the Freemasons merely to associate with the wealthy young Brothers who were influential through their connections or rank, and of whom there were very many in the lodge. Pierre began to feel dissatisfied with what he was doing. Freemasonry, at any rate as he saw it here, sometimes seemed to him based merely on externals. He did not think of doubting Freemasonry itself, but suspected that Russian Masonry had taken a wrong path and deviated from its original principles. And so toward the end of the year he went abroad to be initiated into the higher secrets of the order. What is to be done in these circumstances? To favor revolutions, overthrow everything, repel force by force? No! We are very far from that. Every violent reform deserves censure, for it quite fails to remedy evil while men remain what they are, and also because wisdom needs no violence. "But what is there in running across it like that?" said Ilagin's groom. "Once she had missed it and turned it away, any mongrel could take it," Ilagin was saying at the same time, breathless from his gallop and his excitement.就不会给你什么。即使不考虑句子的顺序，你也会得到0个正结果：

str.split('\n')

Python re.split（）vs nltk word_tokenize和sent_tokenize

1 个答案: