Natural language processing (NLP) explained

Written by Neri Van Otten

Neri Van Otten is an experienced data scientist, software engineer, author and mentor. She has a special interest in natural language processing - automatically understanding large amounts of documents or text and making sure processes scale so that large amounts of text become truly useful.

April 7, 2022

What is natural language processing?

Natural language processing (NLP) is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Where is NLP used?

Applications of natural language processing include machine translation, speech recognition, text classification, topic modelling, information extraction, question answering, chatbots, and machine reading comprehension.

NLP algorithms are used in a wide variety of applications where automated processing of human language is desired. Many search engines use NLP techniques to understand queries and documents. Social media sites such as Facebook and Twitter use NLP to target ads and suggest friends or followers. Amazon uses NLP to recommend products to customers and generate product descriptions. Microsoft Word uses NLP to check spelling and grammar.

Where is NLP heading?

Deep learning techniques have been particularly successful in natural language processing, leading to significant advances in the field. These techniques are now used in a wide variety of applications, including machine translation, speech recognition, and question answering.

In the future, natural language processing will continue to play an important role in many areas of computer science and artificial intelligence. As data sets become larger and more diverse, NLP will become increasingly important for extracting meaning from text and other forms of data

NLP in document analytics

NLP can be used for document analytics in a variety of ways. For instance, NLP can be used to extract information from unstructured text, such as customer reviews or social media posts. This information can then be used to generate insights about customers or products. Additionally, NLP can be used to automatically classify documents into categories, such as spam or non-spam. This can be useful for identifying important documents or filtering out irrelevant ones.

NLP can also be used to generate summaries of long documents, such as legal contracts or medical records. This can save time by allowing people to quickly get an overview of a document without having to read it in its entirety. Additionally, NLP can be used to identify key phrases or terms in a document, which can be useful for search or information retrieval.

There are many other potential applications of NLP in document analytics. For instance, NLP can be used to automatically translate documents into different languages. This can be useful for making documents more accessible to a wider audience. Additionally, NLP can be used to identify relationships between entities within a document, such as people, places, or things. This can be useful for identifying connections between different pieces of information.

NLP research

Natural language processing is an active area of research, with new techniques and applications being developed all the time. Some current research topics include machine translation, question answering, dialogue systems, and text generation. Additionally, researchers are working on ways to improve the accuracy of NLP models and make them more efficient.

At the Doc Hawk we constantly improve our models by actively engaging in NLP research. Would you like to find out more? Leave a comment or send us a message.

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