Reports‌what is natural language processing EN
Created by Bernard Huang
Last edited
Content grade
F
Suggested: A+
Word count
116
Typical: 2,390
Readability
10-12th grade
Typical: College
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Brief Templates

What Is Natural Language Processing?

Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.

The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.

In this post, you will discover what natural language processing is and why it is so important.

After reading this post, you will know:

  • What natural language is and how it is different from other types of data.
  • What makes working with natural language so challenging.
  • Where the field of NLP came from and how it is defined by modern practitioners.
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natural language processing
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nlp
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machine learning
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artificial intelligence
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semantic
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human language
Typical: 3–7
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algorithms
Typical: 2–6
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sentiment analysis
Typical: 2–4
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english
Typical: 1–3
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chatbots
Typical: 2–4
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python
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deep learning
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machine translation
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social media
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summarization
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computer science
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computational linguistics
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speech recognition
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alexa
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unstructured data
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natural language understanding
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siri
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syntax
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search engines
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neural networks
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real-world
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natural language generation
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tokenization
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stemming
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part-of-speech tagging
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tutorials
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parsing
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amazon
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lemmatization
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named entity recognition
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nlu
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disambiguation
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machine learning methods
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semantic analysis
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data sets
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topic modeling
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use cases
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nlp systems
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virtual assistants
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text summarization
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processing tasks
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nlp algorithms
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healthcare
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nltk
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text data
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statistical methods
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automation
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nlp models
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nlp applications
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language models
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nlp tasks
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question answering
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nlp technology
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natural language processing

natural-language-processing, nat­ur­al lan­guage pro­cessing, natural_language_processing
Importance: 10/10
Heading presence: ‌ High
Typical uses: 6–18
Current uses: 4

nlp

nlps
Importance: 10/10
Heading presence: ‌ High
Typical uses: 11–31
Current uses: 2

machine learning

machine-learning, machine_learning
Importance: 10/10
Heading presence: ‌ Moderate
Typical uses: 2–6
Current uses: -

artificial intelligence

Importance: 9/10
Typical uses: 1–3
Current uses: -

semantic

semantics
Importance: 9/10
Typical uses: 2–4
Current uses: -

human language

human languages, hu­man lan­guage, human-language
Importance: 8/10
Typical uses: 3–7
Current uses: -

algorithms

algorithm
Importance: 8/10
Typical uses: 2–6
Current uses: -

sentiment analysis

Importance: 7/10
Typical uses: 2–4
Current uses: -

english

Importance: 7/10
Typical uses: 1–3
Current uses: -

chatbots

chatbot, chat bots
Importance: 7/10
Typical uses: 2–4
Current uses: -

python

Importance: 7/10
Typical uses: 2–4
Current uses: -

deep learning

deeplearning, deep-learning
Importance: 6/10
Typical uses: 2–6
Current uses: -

machine translation

Importance: 6/10
Typical uses: 1–3
Current uses: -

social media

Importance: 6/10
Typical uses: 1–3
Current uses: -

summarization

Importance: 5/10
Typical uses: 1–2
Current uses: -

computer science

Importance: 5/10
Typical uses: 1–2
Current uses: -

computational linguistics

Importance: 5/10
Typical uses: 1–2
Current uses: -

speech recognition

Importance: 5/10
Typical uses: 1–2
Current uses: -

alexa

Importance: 5/10
Typical uses: 1–2
Current uses: -

unstructured data

Importance: 5/10
Typical uses: 1–3
Current uses: -

natural language understanding

Importance: 5/10
Typical uses: 1–3
Current uses: -

siri

Importance: 5/10
Typical uses: 1–2
Current uses: -

syntax

syntaxes
Importance: 5/10
Typical uses: 1–2
Current uses: -

search engines

search engine
Importance: 5/10
Typical uses: 1–3
Current uses: -

neural networks

neural network
Importance: 5/10
Typical uses: 1–3
Current uses: -

real-world

real world
Importance: 5/10
Typical uses: 1–2
Current uses: -

natural language generation

natural-language generation
Importance: 4/10
Typical uses: 1–3
Current uses: -

tokenization

tokenizations
Importance: 4/10
Typical uses: 1–2
Current uses: -

stemming

Importance: 4/10
Typical uses: 1–3
Current uses: -

part-of-speech tagging

part of speech tagging, part-of-speech-tagging
Importance: 4/10
Typical uses: 1–2
Current uses: -

tutorials

tutorial
Importance: 4/10
Typical uses: 1–3
Current uses: -

parsing

Importance: 4/10
Typical uses: 1–2
Current uses: -

amazon

Importance: 4/10
Typical uses: 1–3
Current uses: -

lemmatization

Importance: 4/10
Typical uses: 1–2
Current uses: -

named entity recognition

Importance: 4/10
Typical uses: 1–3
Current uses: -

nlu

Importance: 4/10
Typical uses: 1–3
Current uses: -

disambiguation

Importance: 4/10
Typical uses: 1–2
Current uses: -

machine learning methods

Importance: 3/10
Typical uses: 1–2
Current uses: -

semantic analysis

Importance: 3/10
Typical uses: 1–2
Current uses: -

data sets

datasets, dataset, data set
Importance: 3/10
Typical uses: 1–2
Current uses: -

topic modeling

Importance: 3/10
Typical uses: 1–2
Current uses: -

use cases

use case
Importance: 3/10
Typical uses: 1–3
Current uses: -

nlp systems

nlp system
Importance: 3/10
Typical uses: 1–2
Current uses: -

virtual assistants

virtual assistant
Importance: 3/10
Typical uses: 1–2
Current uses: -

text summarization

Importance: 3/10
Typical uses: 1–2
Current uses: -

processing tasks

processing task
Importance: 3/10
Typical uses: 1–2
Current uses: -

nlp algorithms

nlp algorithm
Importance: 3/10
Typical uses: 1–2
Current uses: -

healthcare

health care
Importance: 3/10
Typical uses: 2–6
Current uses: -

nltk

Importance: 3/10
Typical uses: 2–4
Current uses: -

text data

Importance: 3/10
Typical uses: 1–2
Current uses: -

statistical methods

Importance: 3/10
Typical uses: 1–2
Current uses: -

big data

Importance: 3/10
Typical uses: 2–6
Current uses: -

automation

Importance: 3/10
Typical uses: 1–2
Current uses: -

nlp models

nlp model
Importance: 3/10
Typical uses: 1–2
Current uses: -

nlg

Importance: 3/10
Typical uses: 1–3
Current uses: -

nlp applications

nlp application
Importance: 3/10
Typical uses: 1–3
Current uses: -

language models

language model
Importance: 3/10
Typical uses: 1–3
Current uses: -

nlp tasks

nlp task
Importance: 3/10
Typical uses: 1–3
Current uses: -

question answering

question-answering
Importance: 3/10
Typical uses: 1–3
Current uses: -

nlp technology

Importance: 3/10
Typical uses: 1–2
Current uses: -

segmentation

Importance: 3/10
Typical uses: 1–3
Current uses: -

ibm

Importance: 3/10
Typical uses: 1–3
Current uses: -

python library

python libraries
Importance: 3/10
Typical uses: 1–2
Current uses: -

data scientists

data scientist, datascientist
Importance: 3/10
Typical uses: 1–2
Current uses: -

branch of artificial intelligence

Importance: 3/10
Typical uses: 1–2
Current uses: -