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Python Natural Language Processing

You're reading from   Python Natural Language Processing Advanced machine learning and deep learning techniques for natural language processing

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787121423
Length 486 pages
Edition 1st Edition
Languages
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Author (1):
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Jalaj Thanaki Jalaj Thanaki
Author Profile Icon Jalaj Thanaki
Jalaj Thanaki
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Table of Contents (13) Chapters Close

Preface 1. Introduction FREE CHAPTER 2. Practical Understanding of a Corpus and Dataset 3. Understanding the Structure of a Sentences 4. Preprocessing 5. Feature Engineering and NLP Algorithms 6. Advanced Feature Engineering and NLP Algorithms 7. Rule-Based System for NLP 8. Machine Learning for NLP Problems 9. Deep Learning for NLU and NLG Problems 10. Advanced Tools 11. How to Improve Your NLP Skills 12. Installation Guide

Handling ambiguity

When we jump into semantic analysis, we may find there are many cases that are too ambiguous for an NLP system to handle. In these cases, we need to know what kinds of ambiguity exist and how we can handle them.

Ambiguity is one of the areas of NLP and cognitive sciences that doesn't have a well-defined solution. Sometimes, sentences are so complex and ambiguous that only the speaker can define the original or definite meaning of the sentence.

A word, phrase, or sentence is ambiguous if it has more than one meaning. If we consider word light,than it can mean not very heavy or not very dark. This is word level ambiguity. The phrase porcelain egg container is structure level ambiguity. So, here we will see different types of ambiguities in NLP .

First, let's see the types of ambiguity, and then see how to handle them by using the means that are available...

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