Part of Speech Tagging with NLTK

One of the more powerful aspects of the NLTK module is the Part of Speech tagging that it can do for you. This means labeling words in a sentence as nouns, adjectives, verbs...etc. Even more impressive, it also labels by tense, and more. Here's a list of the tags, what they mean, and some examples:

POS tag list:

CC	coordinating conjunction
CD	cardinal digit
DT	determiner
EX	existential there (like: "there is" ... think of it like "there exists")
FW	foreign word
IN	preposition/subordinating conjunction
JJ	adjective	'big'
JJR	adjective, comparative	'bigger'
JJS	adjective, superlative	'biggest'
LS	list marker	1)
MD	modal	could, will
NN	noun, singular 'desk'
NNS	noun plural	'desks'
NNP	proper noun, singular	'Harrison'
NNPS	proper noun, plural	'Americans'
PDT	predeterminer	'all the kids'
POS	possessive ending	parent's
PRP	personal pronoun	I, he, she
PRP$	possessive pronoun	my, his, hers
RB	adverb	very, silently,
RBR	adverb, comparative	better
RBS	adverb, superlative	best
RP	particle	give up
TO	to	go 'to' the store.
UH	interjection	errrrrrrrm
VB	verb, base form	take
VBD	verb, past tense	took
VBG	verb, gerund/present participle	taking
VBN	verb, past participle	taken
VBP	verb, sing. present, non-3d	take
VBZ	verb, 3rd person sing. present	takes
WDT	wh-determiner	which
WP	wh-pronoun	who, what
WP$	possessive wh-pronoun	whose
WRB	wh-abverb	where, when

How might we use this? While we're at it, we're going to cover a new sentence tokenizer, called the PunktSentenceTokenizer. This tokenizer is capable of unsupervised machine learning, so you can actually train it on any body of text that you use. First, let's get some imports out of the way that we're going to use:

import nltk
from nltk.corpus import state_union
from nltk.tokenize import PunktSentenceTokenizer

Now, let's create our training and testing data:

train_text = state_union.raw("2005-GWBush.txt")
sample_text = state_union.raw("2006-GWBush.txt")

One is a State of the Union address from 2005, and the other is from 2006 from past President George W. Bush.

Next, we can train the Punkt tokenizer like:

custom_sent_tokenizer = PunktSentenceTokenizer(train_text)

Then we can actually tokenize, using:

tokenized = custom_sent_tokenizer.tokenize(sample_text)

Now we can finish up this part of speech tagging script by creating a function that will run through and tag all of the parts of speech per sentence like so:

def process_content():
        for i in tokenized[:5]:
            words = nltk.word_tokenize(i)
            tagged = nltk.pos_tag(words)

    except Exception as e:


The output should be a list of tuples, where the first element in the tuple is the word, and the second is the part of speech tag. It should look like:

[('PRESIDENT', 'NNP'), ('GEORGE', 'NNP'), ('W.', 'NNP'), ('BUSH', 'NNP'), ("'S", 'POS'), ('ADDRESS', 'NNP'), ('BEFORE', 'NNP'), ('A', 'NNP'), ('JOINT', 'NNP'), ('SESSION', 'NNP'), ('OF', 'NNP'), ('THE', 'NNP'), ('CONGRESS', 'NNP'), ('ON', 'NNP'), ('THE', 'NNP'), ('STATE', 'NNP'), ('OF', 'NNP'), ('THE', 'NNP'), ('UNION', 'NNP'), ('January', 'NNP'), ('31', 'CD'), (',', ','), ('2006', 'CD'), ('THE', 'DT'), ('PRESIDENT', 'NNP'), (':', ':'), ('Thank', 'NNP'), ('you', 'PRP'), ('all', 'DT'), ('.', '.')] [('Mr.', 'NNP'), ('Speaker', 'NNP'), (',', ','), ('Vice', 'NNP'), ('President', 'NNP'), ('Cheney', 'NNP'), (',', ','), ('members', 'NNS'), ('of', 'IN'), ('Congress', 'NNP'), (',', ','), ('members', 'NNS'), ('of', 'IN'), ('the', 'DT'), ('Supreme', 'NNP'), ('Court', 'NNP'), ('and', 'CC'), ('diplomatic', 'JJ'), ('corps', 'NNS'), (',', ','), ('distinguished', 'VBD'), ('guests', 'NNS'), (',', ','), ('and', 'CC'), ('fellow', 'JJ'), ('citizens', 'NNS'), (':', ':'), ('Today', 'NN'), ('our', 'PRP$'), ('nation', 'NN'), ('lost', 'VBD'), ('a', 'DT'), ('beloved', 'VBN'), (',', ','), ('graceful', 'JJ'), (',', ','), ('courageous', 'JJ'), ('woman', 'NN'), ('who', 'WP'), ('called', 'VBN'), ('America', 'NNP'), ('to', 'TO'), ('its', 'PRP$'), ('founding', 'NN'), ('ideals', 'NNS'), ('and', 'CC'), ('carried', 'VBD'), ('on', 'IN'), ('a', 'DT'), ('noble', 'JJ'), ('dream', 'NN'), ('.', '.')] [('Tonight', 'NNP'), ('we', 'PRP'), ('are', 'VBP'), ('comforted', 'VBN'), ('by', 'IN'), ('the', 'DT'), ('hope', 'NN'), ('of', 'IN'), ('a', 'DT'), ('glad', 'NN'), ('reunion', 'NN'), ('with', 'IN'), ('the', 'DT'), ('husband', 'NN'), ('who', 'WP'), ('was', 'VBD'), ('taken', 'VBN'), ('so', 'RB'), ('long', 'RB'), ('ago', 'RB'), (',', ','), ('and', 'CC'), ('we', 'PRP'), ('are', 'VBP'), ('grateful', 'JJ'), ('for', 'IN'), ('the', 'DT'), ('good', 'NN'), ('life', 'NN'), ('of', 'IN'), ('Coretta', 'NNP'), ('Scott', 'NNP'), ('King', 'NNP'), ('.', '.')] [('(', 'NN'), ('Applause', 'NNP'), ('.', '.'), (')', ':')] [('President', 'NNP'), ('George', 'NNP'), ('W.', 'NNP'), ('Bush', 'NNP'), ('reacts', 'VBZ'), ('to', 'TO'), ('applause', 'VB'), ('during', 'IN'), ('his', 'PRP$'), ('State', 'NNP'), ('of', 'IN'), ('the', 'DT'), ('Union', 'NNP'), ('Address', 'NNP'), ('at', 'IN'), ('the', 'DT'), ('Capitol', 'NNP'), (',', ','), ('Tuesday', 'NNP'), (',', ','), ('Jan', 'NNP'), ('.', '.')]

At this point, we can begin to derive meaning, but there is still some work to do. The next topic that we're going to cover is chunking, which is where we group words, based on their parts of speech, into hopefully meaningful groups.

The next tutorial:

  • Tokenizing Words and Sentences with NLTK
  • Stop words with NLTK
  • Stemming words with NLTK
  • Part of Speech Tagging with NLTK
  • Chunking with NLTK
  • Chinking with NLTK
  • Named Entity Recognition with NLTK
  • Lemmatizing with NLTK
  • The corpora with NLTK
  • Wordnet with NLTK
  • Text Classification with NLTK
  • Converting words to Features with NLTK
  • Naive Bayes Classifier with NLTK
  • Saving Classifiers with NLTK
  • Scikit-Learn Sklearn with NLTK
  • Combining Algorithms with NLTK
  • Investigating bias with NLTK
  • Improving Training Data for sentiment analysis with NLTK
  • Creating a module for Sentiment Analysis with NLTK
  • Twitter Sentiment Analysis with NLTK
  • Graphing Live Twitter Sentiment Analysis with NLTK with NLTK
  • Named Entity Recognition with Stanford NER Tagger
  • Testing NLTK and Stanford NER Taggers for Accuracy
  • Testing NLTK and Stanford NER Taggers for Speed
  • Using BIO Tags to Create Readable Named Entity Lists