Get started with natural language processing

Software is going beyond storing and retrieving unstructured information by using NLP to improve user experiences, manage complex information, enable chatbot dialogs, and perform text analytics

Most applications today still work in the world of processing data from structured and semistructured sources. They connect to SQL databases to query information or present information from JSON or XML data sources. Many applications still avoid the complication of parsing and extracting knowledge from unstructured sources such as open text fields, rich text editors, database CLOB (character large object) data types, social media news streams, and full documents from tools like Microsoft Word, Google Docs, and Adobe Acrobat.

But the world of information is largely unstructured. People enter, search, and manage information in a myriad of tools and formats. Modern applications are going beyond just storing and retrieving unstructured information and are incorporating elements of natural language processing (NLP) to improve user experiences, manage complex information, enable chatbot dialogs, and perform text analytics.

What is natural language processing (NLP)

NLP engines are designed to extract data, information, knowledge, and sentiment from blocks of text and documents. They often use a mix of parsing technologies, knowledge data structures, and machine-learning algorithms to extract and present information in comprehensible formats to both people and downstream applications.

NLP engines typically have the following technical components:

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