Chatbots are intelligent agents that engage in a conversation with the humans in order to answer user queries on a certain topic. Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana are some of the examples of chatbots.
Depending upon the functionality, chatbots can be divided into three categories: General purpose chatbots, task-oriented chatbots, and hybrid chatbots. General purpose chatbots are the chatbots that conduct a general discussion with the user (not on any specific topic). Task-oriented chatbots, on the other hand, are designed to perform specialized tasks, for example, to serve as online ticket reservation system or pizza delivery system, etc. Finally, hybrid chatbots are designed for both general and task-oriented discussions.
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You know how to tokenize text, but now what can you do with it? In this tutorial, you will learn how to use regular expressions along with NLTK.
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After learning the basics of nltk and how to manipulate corpora, you will learn important concepts in NLP that you will use throughout the following tutorials.
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After learning about the basics of Text class, you will learn about what is Frequency Distribution and what resources the NLTK library offers.
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In the previous NLTK tutorial, you learned what frequency distribution is. Now, you will learn how what a corpus is and how to use it with NLTK.
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This is the first part of the series that will introduce you to the NLTK module. In this tutorial, you will learn how to set up your NLTK and start with some of the functions in the module.
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Edit Distance (a.k.a. Levenshtein Distance) is a measure of similarity between two strings referred to as the source string and the target string.
The distance between the source string and the target string is the minimum number of edit operations (deletions, insertions, or substitutions) required to transform the source into the target. The lower the distance, the more similar the two strings.
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