# Basics of Part-of-Speech (POS) Tagging

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## What is POS tagging?

Tagging, a kind of classification, is the automatic assignment of the description of the tokens. We call the descriptor s ‘tag’, which represents one of the parts of speech (nouns, verb, adverbs, adjectives, pronouns, conjunction and their sub-categories), semantic information and so on.

On the other hand, if we talk about Part-of-Speech (POS) tagging, it may be defined as the process of converting a sentence in the form of a list of words, into a list of tuples. Here, the tuples are in the form of (word, tag). We can also call POS tagging a process of assigning one of the parts of speech to the given word.

Following table represents the most frequent POS notification used in Penn Treebank corpus −

Sr.No Tag Description
1 NNP Proper noun, singular
2 NNPS Proper noun, plural
3 PDT Pre determiner
4 POS Possessive ending
5 PRP Personal pronoun
6 PRP$Possessive pronoun 7 RB Adverb 8 RBR Adverb, comparative 9 RBS Adverb, superlative 10 RP Particle 11 SYM Symbol (mathematical or scientific) 12 TO to 13 UH Interjection 14 VB Verb, base form 15 VBD Verb, past tense 16 VBG Verb, gerund/present participle 17 VBN Verb, past 18 WP Wh-pronoun 19 WP$ Possessive wh-pronoun
21 # Pound sign
22 \$ Dollar sign
23 . Sentence-final punctuation
24 , Comma
25 : Colon, semi-colon
26 ( Left bracket character
27 ) Right bracket character
28 " Straight double quote
29 ' Left open single quote
30 " Left open double quote
31 ' Right close single quote
32 " Right open double quote

### Example

Let us understand it with a Python experiment −

import nltk
from nltk import word_tokenize
sentence = "I am going to school"
print (nltk.pos_tag(word_tokenize(sentence)))


### Output

[('I', 'PRP'), ('am', 'VBP'), ('going', 'VBG'), ('to', 'TO'), ('school', 'NN')]


## Why POS tagging?

POS tagging is an important part of NLP because it works as the prerequisite for further NLP analysis as follows −

• Chunking
• Syntax Parsing
• Information extraction
• Machine Translation
• Sentiment Analysis
• Grammar analysis & word-sense disambiguation

## TaggerI - Base class

All the taggers reside in NLTK’s nltk.tag package. The base class of these taggers is TaggerI, means all the taggers inherit from this class.

Methods − TaggerI class have the following two methods which must be implemented by all its subclasses −

• tag() method − As the name implies, this method takes a list of words as input and returns a list of tagged words as output.

• evaluate() method − With the help of this method, we can evaluate the accuracy of the tagger.

## The Baseline of POS Tagging

The baseline or the basic step of POS tagging is Default Tagging, which can be performed using the DefaultTagger class of NLTK. Default tagging simply assigns the same POS tag to every token. Default tagging also provides a baseline to measure accuracy improvements.

### DefaultTagger class

Default tagging is performed by using DefaultTagging class, which takes the single argument, i.e., the tag we want to apply.

### How does it work?

As told earlier, all the taggers are inherited from TaggerI class. The DefaultTagger is inherited from SequentialBackoffTagger which is a subclass of TaggerI class. Let us understand it with the following diagram −

As being the part of SeuentialBackoffTagger, the DefaultTagger must implement choose_tag() method which takes the following three arguments.

• Token’s list
• Current token’s index
• Previous token’s list, i.e., the history

### Example

import nltk
from nltk.tag import DefaultTagger
exptagger = DefaultTagger('NN')
exptagger.tag(['Tutorials','Point'])


### Output

[('Tutorials', 'NN'), ('Point', 'NN')]


In this example, we chose a noun tag because it is the most common types of words. Moreover, DefaultTagger is also most useful when we choose the most common POS tag.

## Accuracy evaluation

The DefaultTagger is also the baseline for evaluating accuracy of taggers. That is the reason we can use it along with evaluate() method for measuring accuracy. The evaluate() method takes a list of tagged tokens as a gold standard to evaluate the tagger.

Following is an example in which we used our default tagger, named exptagger, created above, to evaluate the accuracy of a subset of treebank corpus tagged sentences −

### Example

import nltk
from nltk.tag import DefaultTagger
exptagger = DefaultTagger('NN')
from nltk.corpus import treebank
testsentences = treebank.tagged_sents() [1000:]
exptagger.evaluate (testsentences)


### Output

0.13198749536374715


The output above shows that by choosing NN for every tag, we can achieve around 13% accuracy testing on 1000 entries of the treebank corpus.

## Tagging a list of sentences

Rather than tagging a single sentence, the NLTK’s TaggerI class also provides us a tag_sents() method with the help of which we can tag a list of sentences. Following is the example in which we tagged two simple sentences

### Example

import nltk
from nltk.tag import DefaultTagger
exptagger = DefaultTagger('NN')
exptagger.tag_sents([['Hi', ','], ['How', 'are', 'you', '?']])


### Output

[
[
('Hi', 'NN'),
(',', 'NN')
],
[
('How', 'NN'),
('are', 'NN'),
('you', 'NN'),
('?', 'NN')
]
]


In the above example, we used our earlier created default tagger named exptagger.

## Un-tagging a sentence

We can also un-tag a sentence. NLTK provides nltk.tag.untag() method for this purpose. It will take a tagged sentence as input and provides a list of words without tags. Let us see an example −

### Example

import nltk
from nltk.tag import untag
untag([('Tutorials', 'NN'), ('Point', 'NN')])


### Output

['Tutorials', 'Point']