This document provides an overview of natural language processing (NLP). It discusses what NLP is, its importance in bridging communication between humans and computers, and its essential elements. The essential elements covered are text preprocessing, morphological analysis, syntactic analysis, semantic analysis, discourse analysis, and pragmatic analysis. The document also discusses techniques in NLP such as rule-based and statistical techniques. It briefly mentions algorithms in NLP including machine learning methods and pre-trained language models. Finally, it states that NLP has various applications.
2. CONTENTS
• Introduction
What is natural language processing (NLP) and
why is it important?
The development of NLP and its historical
background
• Essential elements of NLP
• Techniques and Algorithms for NLP
• Applications of NLP
• Challenges and Future of NLP
3. WHAT IS NLP, AND WHY IS
IT IMPORTANT?
• Natural language Processing (NLP) is a
subfield of artificial intelligence (AI).
• NLP studies how well computers can
comprehend, analyze, and produce human
language.
• NLP is essential in bridging the
communication gap between people and machines,
allowing for more natural and effective
communication.
7. MORPHOLOGICAL
ANALYSIS
• Morphological analysis, including tokenization,
stemming, and lemmatization.
• Breaking words down into their component pieces, such
as tokens, stems, and lemmas, is known as
morphological analysis.
• Tokenization breaks down text into smaller pieces, like
words or subwords.
• Stemming eliminates word variants caused by tense or
pluralization and reduces words to their root form. By
mapping words to their root or dictionary form,
lemmatization takes lemmatization a step further.
11. PRAGMATIC ANALYSIS
THE PRAGMATIC ANALYSIS
FOCUSES ON
INTERPRETING LANGUAGE
CONSIDERING ITS
INTENDED MEANING AND
COMMUNICATIONAL
CONTEXT.
SPEECH ACT RECOGNITION
ATTEMPTS TO ASCERTAIN
THE PURPOSE OF A
STATEMENT, SUCH AS
DEMAND, QUERY, OR
INSTRUCTION.
DIALOGUE MANAGEMENT
ENTAILS CONTROLLING
THE FLOW OF DIALOGUE
AND PRODUCING SUITABLE
RESPONSES.
12. TECHNIQUES IN NLP
Rule-based techniques: NLP uses constructed linguistic rules and
patterns to analyze text. These guidelines were created by experts and
may not apply well to all contexts.
Statistical techniques: It use probabilistic models and algorithms for
text evaluation. Examples of statistical methodologies include n-gram
models, hidden Markov models, and conditional random fields.
13. ALGORITHMS IN NLP
Methods for machine learning, such as
deep learning and neural networks
Pre-trained language models and
transfer learning, such as BERT and
GPT
