Named entity recognition (ner) with nltk

Named Entity Recognition (NER) with NLTK

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Named Entity Recognition with NLTK :
Natural language processing is a sub-area of computer science, information
engineering, and artificial intelligence concerned with the interactions between
computers and human (native) languages. This is nothing but how to program
computers to process and analyse large amounts of natural language data.
NLP = Computer Science + AI + Computational Linguistics
n another way, Natural language processing is the capability of computer software
to understand human language as it is spoken. NLP is one of the component of
artificial intelligence (AI).

About NLTK
•The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and
programs for symbolic and statistical natural language processing (NLP) for English
written in the Python programming language.
•It was developed by Steven Bird and Edward Loper in the Department of Computer
and Information Science at the University of Pennsylvania.
•A software package for manipulating linguistic data and performing NLP tasks.

Named Entity Recognition (NER)
Named Entity Recognition is used in many fields in Natural Language Processing
(NLP), and it can help answering many real-world questions.
Named entity recognition(NER) is probably the first step towards information
extraction that seeks to locate and classify named entities in text into pre-defined
categories such as the names of persons, organizations, locations, expressions of
times, quantities, monetary values, percentages, etc.
Information comes in many shapes and sizes.
One important form is structured data, where there is a regular and predictable
organization of entities and relationships.

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For example, we might be interested in the relation between companies and
locations.
Given a company, we would like to be able to identify the locations where it does
business; conversely, given a location, we would like to discover which companies
do business in that location. Our data is in tabular form, then answering these
queries is straightforward.
Org Name Location Name
TCS PUNE
INFOCEPT PUNE
WIPRO PUNE
AMAZON HYDERABAD
INTEL HYDERABAD

If this location data was stored in Python as a list of tuples (entity, relation, entity),
then the question “Which organizations operate in HYDERABAD?” could be given as
follows:
>>> import nltk
>>> loc=[('TCS', 'IN', 'PUNE’),
... ('INFOCEPT', 'IN', 'PUNE’),
... ('WIPRO', 'IN', 'PUNE’),
... ('AMAZON', 'IN', 'HYDERABAD’) ,
... ('INTEL', 'IN', 'HYDERABAD’),
... ]

>>> query = [e1 for (e1, rel, e2) in loc if e2=='HYDERABAD’]
>>> print(query)
['AMAZON', 'INTEL’]
>>> query = [e1 for (e1, rel, e2) in loc if e2=='PUNE’]
>>> print(query)
['TCS', 'INFOCEPT', 'WIPRO']

Information Extraction has many applications, including business intelligence,
resume harvesting, media analysis, sentiment detecti on, patent search, and email
scanning. A particularly important area of current research involves the attempt to
extract structured data out of electronically-available scientific literature, especially
in the domain of biology and medicine.
Information Extraction Architecture
Following figure shows the architecture for Information extraction system.

The above system takes the raw text of a document as an input, and produces a list
of (entity, relation, entity) tuples as its output. For example, given a document that
indicates that the company INTEL is in HYDERABAD it might generate the tuple
([ORG: ‘INTEL’] ‘in’ [LOC: ‘ HYDERABAD’]). The steps in the information extraction
system is as follows.
STEP 1: The raw text of the document is split into sentences using a sentence
segmentation.
STEP 2: Each sentence is further subdivided into words using a tokenization.
STEP 3: Each sentence is tagged with part-of-speech tags, which will prove very
helpful in the next step, named entity detection.

STEP 4: In this step, we search for mentions of potentially interesting entities in
each sentence.
STEP 5: we use relation detection to search for likely relations between different
entities in the text.
Chunking
The basic technique that we use for entity detection is chunking which segments
and labels multi-token sequences.

In the following figure shows the Segmentation and Labelling at both the Token
and Chunk Levels, the smaller boxes in it show the word-level tokenization and
part-of-speech tagging, while the large boxes show higher-level chunking. Each of
these larger boxes is called a chunk. Like tokenization, which omits whitespace,
chunking usually selects a subset of the tokens. Also, like tokenization, the pieces
produced by a chunker do not overlap in the source text.

Noun Phrase Chunking
In the noun phrase chunking, or NP-chunking, we will search for chunks
corresponding to individual noun phrases. For example, here is some Wall Street
Journal text with NP-chunks marked using brackets:
[ The/DT market/NN ] for/IN [ system-management/NN software/NN ] for/IN [
Digital/NNP ] [ 's/POS hardware/NN ] is/VBZ fragmented/JJ enough/RB that/IN [ a/DT
giant/NN ] such/JJ as/IN [ Computer/NNP Associates/NNPS ] should/MD do/VB well/RB
there/RB ./.

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NP-chunks are often smaller pieces than complete noun phrases.
One of the most useful sources of information for NP-chunking is part-of-speech
tags.
This is one of the inspirations for performing part-of-speech tagging in our
information extraction system. We determine this approach using an example
sentence. In order to create an NP-chunker, we will first define a chunk grammar,
consisting of rules that indicate how sentences should be chunked. In this case, we
will define a simple grammar with a single regular-expression rule. This rule says
that an NP chunk should be formed whenever the chunker finds an optional
determiner (DT) followed by any number of adjectives (JJ) and then a noun (NN).
Using this grammar, we create a chunk parser , and test it on our example sentence.
The result is a tree, which we can either print, or display graphically.

>> sentence = [("the", "DT"), ("little", "JJ"), ("yellow", "JJ"),
... ("dog", "NN"), ("barked", "VBD"), ("at", "IN"), ("the", "DT"), ("cat", "NN")]
>>> grammar = "NP: {<DT>?<JJ>*<NN>}“
>>> cp = nltk.RegexpParser(grammar)
>>> result = cp.parse(sentence)
>>> print(result)
(S
(NP the/DT little/JJ yellow/JJ dog/NN)
barked/VBD
at/IN
(NP the/DT cat/NN))
>>> result.draw()

Chunking with Regular Expressions
To find the chunk structure for a given sentence, the Regexp Parser chunker starts
with a flat structure in which no tokens are chunked. The chunking rules applied in
turn, successively updating the chunk structure. Once all the rules have been
invoked, the resulting chunk structure is returned. Following simple chunk grammar
consisting of two rules. The first rule matches an optional determiner or possessive
pronoun, zero or more adjectives, then a noun. The second rule matches one or
more proper nouns. We also define an example sentence to be chunked and run the
chunker on this input.

>>> import nltk
>>> grammar = r""" NP: {<DT|PP$>?<JJ>*<NN>}
... {<NNP>+}
... """
>>> cp = nltk.RegexpParser(grammar)
>>> sentence = [("Rapunzel", "NNP"), ("let", "VBD"), ("down", "RP"),
... ("her", "PP$"), ("long", "JJ"), ("golden", "JJ"), ("hair", "NN")]
>>> print(cp.parse(sentence))

OUTPUT:
(S
(NP Rapunzel/NNP)
let/VBD
down/RP
(NP her/PP$ long/JJ golden/JJ hair/NN))

chunk.conllstr2tree() Function:
A conversion function chunk.conllstr2tree() is used to builds a tree representation
from one of these multi-line strings. Moreover, it permits us to choose any subset of
the three chunk types to use, here just for NP chunks:
>>> text = ''' ...
he PRP B-NP
... accepted VBD B-VP
... the DT B-NP
... position NN I-NP
... of IN B-PP
... vice NN B-NP
... chairman NN I-NP

... of IN B-PP
... Carlyle NNP B-NP
... Group NNP I-NP
... , , O
... a DT B-NP
... merchant NN I-NP
... banking NN I-NP
... concern NN I-NP
.. . . O ...
''' >>> nltk.chunk.conllstr2tree(text, chunk_types=['NP']).draw()

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Named entity recognition (ner) with nltk

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