What is NLTK POS tagger asking me to download?

丶灬走出姿态 提交于 2019-11-27 20:27:00
Pearl

When you type nltk.download() in Python, an NLTK Downloader interface gets displayed automatically.
Click on Models and choose maxent_treebank_pos_. It gets installed automatically.

import nltk 
text=nltk.word_tokenize("We are going out.Just you and me.")
print nltk.pos_tag(text)
[('We', 'PRP'), ('are', 'VBP'), ('going', 'VBG'), ('out.Just', 'JJ'),
 ('you', 'PRP'), ('and', 'CC'), ('me', 'PRP'), ('.', '.')]

From NLTK versions higher than v3.2, please use:

>>> import nltk
>>> nltk.__version__
'3.2.1'
>>> nltk.download('averaged_perceptron_tagger')
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /home/alvas/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-date!
True

For NLTK versions using the old MaxEnt model, i.e. v3.1 and below, please use:

>>> import nltk
>>> nltk.download('maxent_treebank_pos_tagger')
[nltk_data] Downloading package maxent_treebank_pos_tagger to
[nltk_data]     /home/alvas/nltk_data...
[nltk_data]   Package maxent_treebank_pos_tagger is already up-to-date!
True

For more details on the change in the default pos_tag, please see https://github.com/nltk/nltk/pull/1143

From the shell/terminal, you can use:

python -m nltk.downloader maxent_treebank_pos_tagger

(might need to be sudo on Linux)

It will install maxent_treebank_pos_tagger (i.e. the standard treebank POS tagger in NLTK) and fix your issue.

nltk.download()

Click on Models and choose maxent_treebank_pos_. It gets installed automatically.

import nltk 
text=nltk.word_tokenize("We are going out.Just you and me.")
print nltk.pos_tag(text)
[('We', 'PRP'), ('are', 'VBP'), ('going', 'VBG'), ('out.Just', 'JJ'),
 ('you', 'PRP'), ('and', 'CC'), ('me', 'PRP'), ('.', '.')]
akshayb
import nltk
text = "Obama delivers his first speech."

sent  =  nltk.sent_tokenize(text)


loftags = []
for s in sent:
    d = nltk.word_tokenize(s)   

    print nltk.pos_tag(d)

Result :

akshayy@ubuntu:~/summ$ python nn1.py [('Obama', 'NNP'), ('delivers', 'NNS'), ('his', 'PRP$'), ('first', 'JJ'), ('speech', 'NN'), ('.', '.')]

( I just asked another question where used this code )

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