Natural Language Processing and Beyond: A Series on Language Processing Patterns

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human (natural) languages. NLP has a wide range of applications, including:

• Text summarization
• Machine translation
• Spam filtering
• Sentiment analysis
• Question answering
• Chatbots
• Language modeling

In this series of articles, we will explore the fundamental concepts of NLP and discuss some of the most common NLP tasks. We will also provide code examples in Python to help you get started with NLP.

Before we dive into the details of NLP, let's first discuss some of the key concepts involved.

Social Media Content Analysis:Natural Language Processing and Beyond (Series on Language Processing, Pattern Recognition, and Intelligent Systems Book 3)

by Alessandro Ghebreigziabiher

4.2 out of 5

Language	:	English
File size	:	16027 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	487 pages

FREE

• Natural language: Natural language is any language that is spoken or written by humans. It is contrasted with artificial languages, which are created for specific purposes (such as programming).
• Tokenization: Tokenization is the process of breaking down a text into individual units, such as words or characters.
• Stemming: Stemming is the process of reducing a word to its root form. For example, the words "running," "ran," and "runs" would all be stemmed to the root word "run."
• Lemmatization: Lemmatization is similar to stemming, but it takes into account the context of the word. For example, the word "run" would be lemmatized to "run" in the sentence "I am running," but it would be lemmatized to "runs" in the sentence "The car runs."
• Part-of-speech tagging: Part-of-speech tagging is the process of assigning a grammatical category to each word in a sentence. For example, the word "the" would be tagged as a determiner, while the word "run" would be tagged as a verb.
• Syntax analysis: Syntax analysis is the process of analyzing the structure of a sentence. It involves identifying the subject, verb, and object of a sentence, as well as the relationships between the different parts of the sentence.
• Semantics: Semantics is the study of the meaning of words and sentences. It involves understanding the relationships between words and concepts, as well as the ways in which words can be combined to create new meanings.

Now that we have a basic understanding of the concepts involved in NLP, let's discuss some of the most common NLP tasks.

• Text summarization: Text summarization is the process of reducing a text to a shorter version that captures the main points.
• Machine translation: Machine translation is the process of translating text from one language to another.
• Spam filtering: Spam filtering is the process of identifying and blocking unwanted email messages.
• Sentiment analysis: Sentiment analysis is the process of determining the emotional tone of a piece of text.
• Question answering: Question answering is the process of answering questions posed in natural language.
• Chatbots: Chatbots are computer programs that simulate human conversation.
• Language modeling: Language modeling is the process of predicting the next word in a sequence of words.

In this article, we have provided a brief overview of natural language processing (NLP). We have discussed the fundamental concepts of NLP, as well as some of the most common NLP tasks. In the next article in this series, we will take a closer look at the task of text summarization.

Social Media Content Analysis:Natural Language Processing and Beyond (Series on Language Processing, Pattern Recognition, and Intelligent Systems Book 3)

by Alessandro Ghebreigziabiher

4.2 out of 5

Language	:	English
File size	:	16027 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	487 pages

FREE