问题描述
我无法理解两者之间的区别.不过，我开始知道 word_tokenize 使用 Penn-Treebank 进行标记化.但 TweetTokenizer 上没有任何内容可用.对于哪种数据，我应该使用 TweetTokenizer 而不是 word_tokenize?
I am unable to understand the difference between the two. Though, I come to know that word_tokenize uses Penn-Treebank for tokenization purposes. But nothing on TweetTokenizer is available. For which sort of data should I be using TweetTokenizer over word_tokenize?
推荐答案
嗯，两个分词器的工作方式几乎相同，将给定的句子拆分为单词.但是您可以将 TweetTokenizer 视为 word_tokenize 的子集.TweetTokenizer 保持主题标签完整，而 word_tokenize 不会.
Well, both tokenizers almost work the same way, to split a given sentence into words. But you can think of TweetTokenizer as a subset of word_tokenize. TweetTokenizer keeps hashtags intact while word_tokenize doesn't.
希望下面的例子能解开你所有的疑惑...
I hope the below example will clear all your doubts...
from nltk.tokenize import TweetTokenizer
from nltk.tokenize import word_tokenize
tt = TweetTokenizer()
tweet = "This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <-- @remy: This is waaaaayyyy too much for you!!!!!!"
print(tt.tokenize(tweet))
print(word_tokenize(tweet))
# output
# ['This', 'is', 'a', 'cooool', '#dummysmiley', ':', ':-)', ':-P', '<3', 'and', 'some', 'arrows', '<', '>', '->', '<--', '@remy', ':', 'This', 'is', 'waaaaayyyy', 'too', 'much', 'for', 'you', '!', '!', '!']
# ['This', 'is', 'a', 'cooool', '#', 'dummysmiley', ':', ':', '-', ')', ':', '-P', '<', '3', 'and', 'some', 'arrows', '<', '>', '-', '>', '<', '--', '@', 'remy', ':', 'This', 'is', 'waaaaayyyy', 'too', 'much', 'for', 'you', '!', '!', '!', '!', '!', '!']
您可以看到 word_tokenize 已将 #dummysmiley 拆分为 '#' 和 'dummysmiley'，而TweetTokenizer 没有，因为 '#dummysmiley'.TweetTokenizer 主要用于分析推文.您可以从此链接了解有关分词器的更多信息
You can see that word_tokenize has split #dummysmiley as '#' and 'dummysmiley', while TweetTokenizer didn't, as '#dummysmiley'. TweetTokenizer is built mainly for analyzing tweets.You can learn more about tokenizer from this link
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