Natural Language Processing(NLP)

Treebank Tag-set


Here are the most important tags used in POS tagging

POS Tag Description Example
CC coordinating conjunction and
CD cardinal number 1, third
DT determiner the
EX existential there there is
FW foreign word d’hoevre
IN preposition/subordinating conjunction in, of, like
JJ adjective green
JJR adjective, comparative greener
JJS adjective, superlative greenest
LS list marker 1)
MD modal could, will
NN noun, singular or mass table
NNS noun plural tables
NNP proper noun, singular John
NNPS proper noun, plural Vikings
PDT predeterminer both the boys
POS possessive ending friend‘s
PRP personal pronoun I, he, it
PRP$ possessive pronoun my, his
RB adverb however, usually, naturally, here, good
RBR adverb, comparative better
RBS adverb, superlative best
RP particle give up
TO to to go, to him
UH interjection uhhuhhuhh
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
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Natural Language Processing(NLP)

What is Part of Speech Tagging or POS tagging?


POS is the process of marking up a word in a text as corresponding to a particular part of speech, based on both its definition, as well as its context.Before we deep down to know about POS tagging its important to know about Parts of Speech.There are mainly 8 part of speech that define the words into different categories. Here is a short summary of Parts of Speech.

part of speech function or “job” example words example sentences
Verb action or state (to) be, have, do, like, work, sing, can, must EnglishClub.com is a web site. I like EnglishClub.com.
Noun thing or person pen, dog, work, music, town, London, teacher, John This is my dog. He lives in my house. We live in London.
Adjective describes a noun a/an, the, 69, some, good, big, red, well, interesting My dog is big. I like big dogs.
Adverb describes a verb, adjective or adverb quickly, silently, well, badly, very, really My dog eats quickly. When he is very hungry, he eats really quickly.
Pronoun replaces a noun I, you, he, she, some Tara is Indian. She is beautiful.
Preposition links a noun to another word to, at, after, on, but We went to school on Monday.
Conjunction joins clauses or sentences or words and, but, when I like dogs and I like cats. I like cats and dogs. I like dogs but I don’t like cats.
Interjection short exclamation, sometimes inserted into a sentence oh!, ouch!, hi!, well Ouch! That hurts! Hi! How are you? Well, I don’t know.

A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token). Let’s take an example,

Input for the POS tagger be,

The strongest rain ever recorded in India shut down the financial hub of Mumbai, snapped communication lines, closed airports and forced thousands of people to sleep in their offices or walk home during the night, officials said today.

Then the output of the POS tagger should look like,

The/DT strongest/JJS rain/NN ever/RB recorded/VBN in/IN India/NNP
shut/VBD down/RP the/DT financial/JJ hub/NN of/IN Mumbai/NNP ,/,
snapped/VBD communication/NN lines/NNS ,/, closed/VBD airports/NNS
and/CC forced/VBD thousands/NNS of/IN people/NNS to/TO sleep/VB in/IN
their/PRP$ offices/NNS or/CC walk/VB home/NN during/IN the/DT night/NN
,/, officials/NNS said/VBD today/NN ./.

Here the NN tag refers to Normal Noun, JJ refers to adjective, etc,. to know more about the tags click here.

Reference:  http://nlp.stanford.edu/software/lex-parser.shtml

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