NLTK: Natural Language Processing made easy

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NLTK

Natural Language Processing made easyElvis Joel D’Souza

Gopikrishnan Nambiar

Ashutosh Pandey

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WHAT: Session Objective

To introduce Natural Language Toolkit(NLTK), an open source library which simplifies the implementation of Natural Language Processing(NLP) in Python.

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HOW: Session Layout

This session is divided into 3 parts:• Python – The programming language• Natural Language Processing (NLP) – The concept• Natural Language Toolkit (NLTK) – The tool for NLP

implementation in Python

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Why Python?

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Data Structures

Python has 4 built-in data structures:1.List2.Tuple3.Dictionary4.Set

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List

• A list in Python is an ordered group of items (or elements).

• It is a very general structure, and list elements don't have to be of the same type.

listOfWords = [‘this’,’is’,’a’,’list’,’of’,’words’]

listOfRandomStuff = [1,’pen’,’costs’,’Rs.’,6.50]

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Tuple

• A tuple in Python is much like a list except that it is immutable (unchangeable) once created.

• They are generally used for data which should not be edited.

Example: (100,10,0.01,’hundred’)

NumberSquare root

ReciprocalNumber in words

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Return a tuple

def func(x,y): # code to compute a and breturn (a,b)

One very useful situation is returning multiple values from a function. To return multiple values in many other languages requires creating an object or container of some type.

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Dictionary• A dictionary in python is a collection of

unordered values which are accessed by key.• Example:

• Here, the key is the character and the value is its position in the alphabet

{1: ‘one’, 2: ‘two’, 3: ‘three’}

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Sets

• Python also has an implementation of the mathematical set. • Unlike sequence objects such as lists and tuples, in which

each element is indexed, a set is an unordered collection of objects.

• Sets also cannot have duplicate members - a given object appears in a set 0 or 1 times.

SetOfBrowsers=set([‘IE’,’Firefox’,’Opera’,’Chrome’])

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Control Statements

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Decision Control - If

num = 3

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Loop Control - While

number = 10

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Loop Control - For

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Functions - Syntax

def functionname(arg1, arg2, ...):statement1 statement2 return variable

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Functions - Example

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Modules

• A module is a file containing Python definitions and statements.

• The file name is the module name with the suffix .py appended.

• A module can be imported by another program to make use of its functionality.

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Import

import math

The import keyword is used to tell Python, that we need the ‘math’ module.

This statement makes all the functions in this module accessible in the program.

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Using Modules – An Example

print math.sqrt(100)

sqrt is a functionmath is a module

math.sqrt(100) returns 10This is being printed to the standard output

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Natural Language Processing

(NLP)

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Natural Language Processing

The term natural language processing encompasses a broad set of techniques for automated generation, manipulation, and analysis of natural or human languages

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Why NLP

• Applications for processing large amounts of texts require NLP expertise

• Index and search large texts• Speech understanding• Information extraction• Automatic summarization

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Stemming

• Stemming is the process for reducing inflected (or sometimes derived) words to their stem, base or root form – generally a written word form.

• The stem need not be identical to the morphological root of the word; it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root.

• When you apply stemming on 'cats', the result is 'cat'

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Part of speech tagging(POS Tagging)

• Part-of-speech (POS) tag: A word can be classified into one or more lexical or part-of-speech categories

• such as nouns, verbs, adjectives, and articles, to name a few. A POS tag is a symbol representing such a lexical category, e.g., NN (noun), VB (verb), JJ (adjective), AT (article).

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POS tagging - continued

• Given a sentence and a set of POS tags, a common language processing task is to automatically assign POS tags to each word in the sentence.

• State-of-the-art POS taggers can achieve accuracy as high as 96%.

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POS Tagging – An Example

The ball is redNOUN VERB

ADJECTIVEARTICLE

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Parsing

Parsing a sentence involves the use of linguistic knowledge of a language to discover the way in which a sentence is structured

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Parsing– An Example

The boy went home

NOUNVERB NOUN

ARTICLE

NP VP

The boy went home

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Challenges

• We will often imply additional information in spoken language by the way we place stress on words.

• The sentence "I never said she stole my money" demonstrates the importance stress can play in a sentence, and thus the inherent difficulty a natural language processor can have in parsing it.

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Depending on which word the speaker places the stress, sentences could have several distinct meanings

Here goes an example…

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• "I never said she stole my money“ Someone else said it, but I didn't.

• "I never said she stole my money“ I simply didn't ever say it.

• "I never said she stole my money" I might have implied it in some way, but I never explicitly said it.

• "I never said she stole my money" I said someone took it; I didn't say it was she.

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• "I never said she stole my money" I just said she probably borrowed it.

• "I never said she stole my money" I said she stole someone else's money.

• "I never said she stole my money" I said she stole something, but not my money

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NLTK

Natural Language Toolkit

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Design Goals

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Exploring Corpora

Corpus is a large collection of text which is used to either train an NLP program or is used as input by an NLP program

In NLTK , a corpus can be loaded using the PlainTextCorpusReader Class

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Loading your own corpus

>>> from nltk.corpus import PlaintextCorpusReadercorpus_root = ‘C:\text\’>>> wordlists = PlaintextCorpusReader(corpus_root, '.*‘)>>> wordlists.fileids()['README', 'connectives', 'propernames', 'web2', 'web2a', 'words']>>> wordlists.words('connectives')['the', 'of', 'and', 'to', 'a', 'in', 'that', 'is', ...]

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NLTK Corpora

• Gutenberg corpus• Brown corpus• Wordnet• Stopwords• Shakespeare corpus• Treebank• And many more…

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Computing with Language: Simple Statistics

Frequency Distributions

>>> fdist1 = FreqDist(text1)>>> fdist1 [2]<FreqDist with 260819 outcomes>>>> vocabulary1 = fdist1.keys()>>> vocabulary1[:50][',', 'the', '.', 'of', 'and', 'a', 'to', ';', 'in', 'that', "'", '-','his', 'it', 'I', 's', 'is', 'he', 'with', 'was', 'as', '"', 'all', 'for','this', '!', 'at', 'by', 'but', 'not', '--', 'him', 'from', 'be', 'on','so', 'whale', 'one', 'you', 'had', 'have', 'there', 'But', 'or', 'were','now', 'which', '?', 'me', 'like']>>> fdist1['whale']906

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Cumulative Frequency Plot for 50 Most Frequently Words in Moby Dick

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POS tagging

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WordNet Lemmatizer

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Parsing

>>> from nltk.parse import ShiftReduceParser>>> sr = ShiftReduceParser(grammar)>>> sentence1 = 'the cat chased the dog'.split()>>> sentence2 = 'the cat chased the dog on the rug'.split()>>> for t in sr.nbest_parse(sentence1):... print t(S (NP (DT the) (N cat)) (VP (V chased) (NP (DT the) (N dog))))

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Authorship Attribution

An Example

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Find nltk @ <python-installation>\Lib\site-packages\nltk

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The Road AheadPython:

• http://www.python.org• A Byte of Python, Swaroop CH

http://www.swaroopch.com/notes/python

Natural Language Processing:• Speech And Language Processing, Jurafsky and Martin• Foundations of Statistical Natural Language Processing,

Manning and Schutze

Natural Language Toolkit:• http://www.nltk.org (for NLTK Book, Documentation)

• Upcoming book by O'reilly Publishers