NLP Tutorial AI with Python | Natural Language Processing

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NLP Tutorial AI with Python
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Contents
NLP Tutorial AI with Python.......................
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.................1 Introduction to Natural
Language Processing...............................
............................3 NLP Tutorial - What
is NLP?...........................................
........................................3 NLP
Tutorial - Components of NLP......................
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.4 a. Natural Language Understanding
(NLU).............................................
.............5 b. Natural Language Generation
(NLG).............................................
..................5 NLP Tutorial - Benefits of
NLP...............................................
.................................6 NLP Tutorial -
Libraries for NLP.................................
............................................6 NLP
Tutorial - Glossary in NLP........................
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.....6 NLP Tutorial - Tasks in NLP................
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..................8 a. Lexical
Analysis..........................................
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.....8 b. Syntactic Analysis...................
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.........................8 c. Semantic
Analysis..........................................
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..8 d. Discourse Integration...................
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.....................9 e. Pragmatic
Analysis..........................................
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.9 NLP Tutorial - NLP Applications................
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...........9 Conclusion NLP Tutorial.............
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NLP Tutorial AI with Python Natural Language
Processing
Introduction to Natural Language Processing
Natural Language Processing is casually dubbed
NLP. It is a field of AI that deals with how
computers and humans interact and how to program
computers to process and analyze huge amounts of
natural language data. This faces some challenges
like speech recognition, natural language
understanding, and natural language
generation. Lets have a look at Python AI
Tutorial Well, NLP is all about developing
applications and services that can understand
human languages.
NLP Tutorial - What is NLP?
In layman terms, NLP is a way for computers to
analyze human language and derive useful meaning
from it. It lets you organize and structure
knowledge to let you perform the following
tasks- Automatic Summarization
Translation
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Named Entity Recognition Relationship
Extraction Sentiment Analysis Speech
Recognition Topic Segmentation NLP analyzes
text and allows machines to understand how we
speak. It considers the hierarchical structure of
language and performs tasks like correcting the
grammar, converting speech to text, and
translating between languages. In computer
science, it is a hard problem. Have a look at
Neural Network in Artificial Intelligence What
do words mean, how do they link together, and
what meaning do they make? The greatest
challenge to NLP is to accurately judge the
intention of words keeping in mind the ambiguity
of the language.
NLP Tutorial - Components of NLP
While talking of NLP in NLP Tutorial, we come
across two main Components of NLP-
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Natural Language Understanding (NLU)
Components of NLP
Natural Language Generation (NLG)
NLP Tutorial - Components of NLP
a. Natural Language Understanding (NLU)
Natural Language Understanding revolves around
machine reading comprehension. This is an AI-hard
problem. An NLU system needs the following
components- Lexicon, Parser, and Grammar
rules. Semantic theory- to guide
comprehension. b. Natural Language Generation
(NLG)
NLG is concerned with generating natural
language. It uses a machine representation system
like a knowledge base or a logical form. You can
think of it as a translator between data and
natural language representation this is the
opposite or NLU. This involves three tasks-
Text Planning- To extract relevant content from
the knowledge base. Sentence Planning- To
choose appropriate words, form meaningful
phrases, and set sentence tone. Text
Realization- To map the sentence plan into
sentence structure.
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NLP Tutorial - Benefits of NLP
Among the numerous benefits of NLP, here, we list
out a few- To improve the efficiency of the
documentation process. NLP is used to
improve the accuracy of the documentation
process. To identify pertinent information
from large databases. Together with Machine
Learning, we dont need to hand-code large sets
of rules.
NLP Tutorial - Libraries for NLP
Many open-source libraries let us work with
Natural Language Programming. Some of those
are- Natural Language ToolKit (NLTK)-
Written in Python allows modules for processing
text, classifying, tokenizing, stemming, parsing,
tagging, and more. Apache OpenNLP- Machine
Learning toolkit allows for tokenizers, sentence
segmentation, part-of-speech tagging, chunking,
parsing, named entity extraction, and more.
Stanford NLP Suite- Tools for part-of-speech
tagging, named entity recognizer, sentiment
analysis, conference resolution system, and
more. Gate NLP Library. MALLET- Java
package for latent dirichlet allocation,
clustering, topic modeling, information
extraction, document classification, and more.
NLP Tutorial - Glossary in NLP
Talking of NLP, we talk
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NLP Tutorial - Glossary in NLP
a. Phonology Study of organizing sound
systematically.
b. Morphology Study of constructing words from
primitive meaningful units.
c. Morpheme Primitive unit of meaning in a
language. d. Syntax Arranging words to form a
sentence determining the structural role of
words in sentences and phrases.
e. Semantics Studying the meanings of words
and combining them to make meaningful phrases and
sentences. f. Pragmatics Using and
understanding sentences in various situations
determining how this affects sentence
interpretation. g. Discourse
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Understanding how a sentence can affect the
next. h. World Knowledge General knowledge about
the world. Do you know about Search Algorithms in
Artificial Intelligence
NLP Tutorial - Tasks in NLP
With Natural Language Processing, we carry out
five different tasks-
a. Lexical Analysis Lexical analysis deals with
identifying and analyzing word structure. We
divide the whole chunk of text into paragraphs,
sentences, and words.
b. Syntactic Analysis Also called parsing, it
involves analyzing words in sentences for grammar
and rearranging them to determine how they relate
to each other. It rejects sentences like The
apple eats the girl.
c. Semantic Analysis
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This deals with extracting the dictionary
meanings from text. It also maps syntactic
structures and objects in the task domain to
check for meaningfulness. It rejects statements
like tall stub. d. Discourse Integration It
analyzes the previous sentence to guess the
meaning of the current sentence and the one after
it. e. Pragmatic Analysis This reinterprets
the statement to ensure it determines correctly
what the statement means. It tries to retrieve
aspects of the language that requires knowledge
of the real world. Have a look at Python Machine
Learning Tutorial
NLP Tutorial - NLP Applications With NLP, we can
do the following- Summarizing blocks of
text. Creating chatbots. Machine
translation. Fighting spam. Extracting
information. Automatically generating
keyword tags. Identifying types of entities
extracted. Identifying the sentiment of a
string with sentiment analysis. Reducing
words to their roots. Summarizing.
Question-answering. Customer service.
Market analysis. So, this was all in NLP
Tutorial. Hope you like our explanation of
Natural Processing Language. Conclusion NLP
Tutorial
And with this, we conclude our introduction to
Natural Language Processing with Python. In this
Natural language Processing Tutorial, we
discussed NLP
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Definition, AI natural language processing, and
example of NLP. Moreover, we talked about its
fundamentals, components, benefits, libraries,
terminologies, tasks, and applications. Next, we
will demonstrate the use of NLTK to implement NLP
with Python. Still, if any doubt regarding NLP
Tutorial, ask in the comment tab.
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