All Courses

AI with Python – Natural Language Processing

Jon Wood

10 months ago

Natural Language Processing | insideAIML
Table of Contents
  • Introduction
  • Components of NLP
               1. Natural Language Understanding (NLU)
               2. Natural Language Generation (NLG)
  • Difficulties in NLU
              1. Lexical ambiguity
              2. Syntax level ambiguity
              3. Referential ambiguity
  • NLP Terminology
  • Steps in NLP
             1. Lexical Analysis
             2. Syntactic Analysis (Parsing)
             3. Semantic Analysis
             4. Discourse Integration
             5. Pragmatic Analysis

Introduction

          Natural Language Processing (NLP) refers to the artificial intelligence (AI) method of communicating with intelligent systems using a natural language such as English.
Processing of Natural Language is required when you want an intelligent system like a robot to perform as per your instructions, when you want to hear a decision from a dialogue based clinical expert system, etc.
The field of NLP involves making computers perform useful tasks with the natural languages humans use.
The input and output of an NLP system can be −
  • Speech
  • Written Text

Components of NLP

Figure. Components of NLP | insideAIML
          In this section, we will learn about the different components of NLP. There are two components of NLP. The components are described below −

1. Natural Language Understanding (NLU)

It involves the following tasks −
  • Mapping the given input in natural language into useful representations.
  • Analyzing different aspects of the language.

2. Natural Language Generation (NLG)

Figure. Natural Language Generation (NLG) | insideAIML
          It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation. It involves −
  • Text planning − This includes retrieving the relevant content from the knowledge base.
  • Sentence planning − This includes choosing the required words, forming meaningful phrases, setting the tone of the sentence.
  • Text Realization − This is mapping sentence plan into sentence structure.

Difficulties in NLU

The NLU is very rich in form and structure; however, it is ambiguous. There can be different levels of ambiguity −

1. Lexical ambiguity

          It is at a very primitive level such as the word-level. For example, treating the word “board” as a noun or a verb?

2. Syntax level ambiguity

          A sentence can be parsed in different ways. For example, “He lifted the beetle with a red cap.” − Did he use a cap to lift the beetle or he lifted a beetle that had a red cap?

3. Referential ambiguity

          Referring to something using pronouns. For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired?

NLP Terminology

Figure. NLP Terminology | insideAIML
Let us now see a few important terms in the NLP terminology.
  • Phonology − It is the study of organizing sound systematically.
  • Morphology − It is a study of the construction of words from primitive meaningful units.
  • Morpheme − It is a primitive unit of meaning in a language.
  • Syntax − It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases.
  • Semantics − It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences.
  • Pragmatics − It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected.
  • Discourse − It deals with how the immediately preceding sentence can affect the interpretation of the next sentence.
  • World Knowledge − It includes general knowledge about the world.

Steps in NLP

Figure. Steps in NLP | insideAIML
This section shows the different steps in NLP.

1. Lexical Analysis

          It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of text into paragraphs, sentences, and words.

2. Syntactic Analysis (Parsing)

          It involves the analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words. The sentence such as “The school goes to the boy” is rejected by an English syntactic analyzer.

3. Semantic Analysis

          It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentences such as “hot ice-cream”.

4. Discourse Integration

          The meaning of any sentence depends upon the meaning of the sentence just before it. In addition, it also brings about the meaning of immediately succeeding sentence.

5. Pragmatic Analysis

          During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real-world knowledge.
  
Liked what you read? Then don’t break the spree. Visit our insideAIML blog page to read more awesome articles. 
Or if you are into videos, then we have an amazing Youtube channel as well. Visit our InsideAIML Youtube Page to learn all about Artificial Intelligence, Deep Learning, Data Science and Machine Learning. 
Keep Learning. Keep Growing. 

Submit Review