Natural language processing (NLP) is an artificial intelligence (AI) technology that allows a machine to recognize and decipher the nuances of human language. It organizes unstructured data by analyzing it for relevance, differences in spellings, correlation, and semantic meaning. It tries to understand different lexicons, grammatical syntaxes, and the relation between words and phrases, just as a human does. It also remembers this organized data.

NLP technologies made a huge leap forward over the past few years, providing more accurate models to handle a variety of tasks: speech to text, text to speech, optical character recognition (OCR), summarization, translation, question answering, language generation, document to text, and more. We see new ways of understanding contexts, people’s sentiments, and behaviors by focusing on understanding human minds from their actions in the digital world. Today, NLP solutions can understand the nuances of human language, such as sentiment and intent, and can generate a humanlike response. The latest research and development (R&D) is in language generation models, such as question generation, abstractive summarization, and visual understanding. This helps in understanding and generating more accurate responses in search results, chatbot interactions, and virtual assistant capabilities, delivering better customer experiences and bringing value to businesses.

Many enterprises are adopting NLP because of the great business and growth opportunities it brings. NLP is one of the most pervasive areas of adoption and growth for deep learning. Improving access to deep learning is very important for fostering competition and innovation in industries, such as banking and financial services, insurance, healthcare, manufacturing, public sector, and so forth. Enterprises in these industries look at NLP for accelerating profitability and operational efficiency to increase competitiveness. The race to the top has commenced, and innovations in large language models continue to push for larger, more complex transformer-based large language models, such as Generative Pretrained Transformer (GPT-3) and its variants. BERT, RoBERTa, ALBERT, XLNet, StructBERT, T5, and OpenAI’s GPT-2 and GPT-3 are the most commonly used pretrained NLP models.

Although these powerful models are enabling enterprises to transform their business in new and impressive ways, they can be challenging to manage because of their size and the time it takes to train them to needed levels of accuracy.

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IDC Perspective: The Business Value of Natural Language Processing Applications in Enterprises

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