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Natural Language Processing Tools
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.
Industry Updates
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:
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