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Natural Language Processing Tools
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Natural Language Processing Tools

Using AI to mimic and understand human language

Overview

Natural Language Processing (NLP) refers to the ability of computers to understand human language in spoken and written formats.

Earlier approaches to NLP were used mainly for machine translation applications and involved a rules-based approach that defined what words and phrases to look for in a text. Instead of hand-written rules, technologies such as AI and deep learning are approaching NLP by ingesting and analyzing large amounts of data and attempting to identify the user's intent to make accurate interpretations.

AI-based NLP solutions today are being used to analyze and proofread documents, automatically transcribe audio and video content, understand sentiment from text, cognitively search data based on user intent, and engage in real-term conversations, especially in the area of customer service.

*Note: Additional sections (such as market sizing, detailed overview, and incumbents) can be provided on request.

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Use cases

Natural Language Processing (NLP) is being increasingly adopted in various industries, such as automotive, media, consumer services, textiles, apparel and luxury goods, and others, to enable different use cases like text understanding and generation, machine translation, sentiment analysis, chatbots and virtual assistants, information archiving and retrieval, and customer inquiry response.

NLP platforms that offer document and sentiment analysis support organizations with document classification, legal document review, customer experience monitoring, and after-sales services. Transcription, cognitive search, and automated translation platforms help global companies with use cases such as content localization, enterprise inventory and customer order navigation, and call center scripting, with plagiarism and proofreading platforms supporting academic integrity and conduct workflows.

We have identified key NLP use cases below:

Market Mapping

Incumbents
Growth
Early
Seed
Pre-Seed
Document analysis
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Transcription
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Sentiment analysis
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Cognitive search
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Conversational AI and customer service automation
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Automated translation
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Plagiarism and proofreading
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Gettranslation
Gettranslation
Gettranslation
Gettranslation
Gettranslation
Gettranslation

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Overview

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What is NLP?

NLP is a field of AI that allows machines to understand and communicate with human language, mimicking human interaction. It comprises two processes: natural language understanding (NLU), which refers to machines understanding incoming audio or text, and natural language generation (NLG), which refers to machines responding via text or speech. Machines take unstructured data in the form of text or speech, convert it into structured, machine-readable formats, process a response, and convert the structured data response into unstructured human language or text/speech we can understand.
Did you know? The concept of NLP had its start in the 1950s with the renowned mathematician Alan Turing proposing what is now known as the Turing Test, which tests the intelligence of a machine by its ability to hold a natural language conversation with a human. Soon after, these capabilities were used by governments to decipher enemy communications and by universities to explore machine translation. The development of NLP was limited in the decades that followed until the evolution of technologies relating to machine learning (ML), deep learning, linguistics, and computer science in recent decades.
NLP has a range of applications and can be used extensively by businesses.
NLP has a range of applications and can be used extensively by businesses.
Not an exhaustive representation of NLP applications for business
Source: Compiled by SPEEDA Edge
How does it work? NLP involves the following main phases: lexical analysis, syntactic analysis, semantic analysis, discourse analysis, and pragmatic analysis, which, in turn, involve several tasks. This is hardly straightforward, as the complexities of human language are many—machines have to be taught to look beyond definitions and grammar rules and consider intent, culture, background, gender, idioms, accents, and subtleties like sarcasm, irony, and humor.
Main phases and common tasks in NLP
Main phases and common tasks in NLP
Source: Compiled by SPEEDA Edge based on various sources
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