S13-Words-for-the-Future-notebooks/nltk-pos-tagger.ipynb

NLTK - Part of Speech¶

In [ ]:

import nltk
import random

In [ ]:

lines = open('txt/language.txt').readlines()
sentence = random.choice(lines)
print(sentence)

Tokens¶

In [ ]:

tokens = nltk.word_tokenize(sentence)
print(tokens)

Part of Speech "tags"¶

In [ ]:

tagged = nltk.pos_tag(tokens)
print(tagged)

Now, you could select for example all the type of verbs:

In [ ]:

selection = []

for word, tag in tagged:
    if 'VB' in tag:
        selection.append(word)

print(selection)

Where do these tags come from?¶

An off-the-shelf tagger is available for English. It uses the Penn Treebank tagset.

From: http://www.nltk.org/api/nltk.tag.html#module-nltk.tag

NLTK provides documentation for each tag, which can be queried using the tag, e.g. nltk.help.upenn_tagset('RB').

From: http://www.nltk.org/book_1ed/ch05.html

In [ ]:

nltk.help.upenn_tagset('PRP')

---

An alphabetical list of part-of-speech tags used in the Penn Treebank Project (link):

Number	Tag	Description
1.	CC	Coordinating conjunction
2.	CD	Cardinal number
3.	DT	Determiner
4.	EX	Existential there
5.	FW	Foreign word
6.	IN	Preposition or subordinating conjunction
7.	JJ	Adjective
8.	JJR	Adjective, comparative
9.	JJS	Adjective, superlative
10.	LS	List item marker
11.	MD	Modal
12.	NN	Noun, singular or mass
13.	NNS	Noun, plural
14.	NNP	Proper noun, singular
15.	NNPS	Proper noun, plural
16.	PDT	Predeterminer
17.	POS	Possessive ending
18.	PRP	Personal pronoun
19.	PRP\$	Possessive pronoun
20.	RB	Adverb
21.	RBR	Adverb, comparative
22.	RBS	Adverb, superlative
23.	RP	Particle
24.	SYM	Symbol
25.	TO	to
26.	UH	Interjection
27.	VB	Verb, base form
28.	VBD	Verb, past tense
29.	VBG	Verb, gerund or present participle
30.	VBN	Verb, past participle
31.	VBP	Verb, non-3rd person singular present
32.	VBZ	Verb, 3rd person singular present
33.	WDT	Wh-determiner
34.	WP	Wh-pronoun
35.	WP$	Possessive wh-pronoun
36.	WRB	Wh-adverb