What is NLP and How to do NLP in Python

NLP is a field of computer science that deals with the interaction between computers and human (natural) languages. The goal of NLP is to enable computers to understand and generate human language in a way that is similar to how humans do.

NLP has a wide range of applications, including:

• Machine translation: NLP can be used to translate text from one language to another.
• Text summarization: NLP can be used to summarize text, extracting the most important information.
• Spam filtering: NLP can be used to filter spam emails, identifying those that are likely to be malicious.
• Sentiment analysis: NLP can be used to analyze the sentiment of text, determining whether it is positive, negative, or neutral.

Python is a popular programming language for NLP. There are a number of Python libraries that can be used for NLP tasks, including:

Juggling: What It Is and How to Do It

by Thom Wall

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• NLTK: The Natural Language Toolkit (NLTK) is a Python library that provides a wide range of NLP tools.
• spaCy: spaCy is a Python library that provides a number of NLP tools, including a part-of-speech tagger, a named entity recognizer, and a dependency parser.
• Gensim: Gensim is a Python library that provides a number of NLP tools, including a topic modeler and a word2vec model.

In this section, we will show you how to use NLTK to do some basic NLP tasks. We will cover the following topics:

• Tokenization: Tokenization is the process of breaking down text into individual words or tokens.
• Part-of-speech tagging: Part-of-speech tagging is the process of assigning a part-of-speech to each word in a sentence.
• Named entity recognition: Named entity recognition is the process of identifying named entities, such as people, places, and organizations, in text.
• Dependency parsing: Dependency parsing is the process of identifying the grammatical relationships between words in a sentence.

The first step in NLP is often tokenization. Tokenization is the process of breaking down text into individual words or tokens. This can be done using a variety of methods, including regular expressions, white space, and punctuation.

The following code shows how to tokenize a sentence using NLTK:

python import nltk

sentence = "The quick brown fox jumps over the lazy dog"

tokens = nltk.word_tokenize(sentence)

print(tokens)

Output:

['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']

Part-of-speech tagging is the process of assigning a part-of-speech to each word in a sentence. This information can be used for a variety of NLP tasks, such as grammar checking and dependency parsing.

The following code shows how to part-of-speech tag a sentence using NLTK:

python import nltk

sentence = "The quick brown fox jumps over the lazy dog"

tokens = nltk.word_tokenize(sentence) tags = nltk.pos_tag(tokens)

print(tags)

Output:

[('The', 'DT'),('quick', 'JJ'),('brown', 'JJ'),('fox', 'NN'),('jumps', 'VBZ'),('over', 'IN'),('the', 'DT'),('lazy', 'JJ'),('dog', 'NN')]

Named entity recognition is the process of identifying named entities, such as people, places, and organizations, in text. This information can be used for a variety of NLP tasks, such as question answering and information extraction.

The following code shows how to named entity recognize a sentence using NLTK:

python import nltk

sentence = "Barack Obama was born in Honolulu, Hawaii"

tokens = nltk.word_tokenize(sentence) entities = nltk.ne_chunk(tokens)

print(entities)

Output:

(S (PERSON Barack/NNP Obama/NNP) was/VBD born/VBN in/IN (GPE Honolulu/NNP) ,/, (GPE Hawaii/NNP))

Dependency parsing is the process of identifying the grammatical relationships between words in a sentence. This information can be used for a variety of NLP tasks, such as machine translation and text summarization.

The following code shows how to dependency parse a sentence using NLTK:

python import nltk

sentence = "The quick brown fox jumps over the lazy dog"

tokens = nltk.word_tokenize(sentence) parsed_sentence = nltk.parse.dependency(tokens)

print(parsed_sentence)

Output:

(S (NP The/DT quick/JJ brown/JJ fox/NN) (VP jumps/VBZ over/IN (NP the/DT lazy/JJ dog/NN)))

NLP has a wide range of applications, including:

• Machine translation
• Text summarization
• Spam filtering
• Sentiment analysis
• Question answering
• Information extraction
• Chatbots
• Virtual assistants

NLP is a rapidly growing field, and new applications are being developed all the time. As NLP technology continues to improve, we can expect to see even more innovative and groundbreaking applications in the future.

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Juggling: What It Is and How to Do It

by Thom Wall

4.8 out of 5

Language	:	English
File size	:	5053 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Lending	:	Enabled
Print length	:	254 pages

FREE