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2020 is a busy year for deep learning based Natural Language Processing (NLP) research. The loudest noise is created by the largest natural language processing (NLP) transformer released to date- GPAT-3. OpenAI's GPAT-3 (175B parameters) is way ahead from the previous record held by Microsoft Research's Turing-NLG at 17B parameters, by about 10 times.
Contemporary developments in NLP require comparatively lesser quantities of training data than ever before. Besides deploying these deep learning models alongside conventional rule-based algorithms for more accurate text analytics, sentiment analysis, conversational AI, and a host of other use cases that explain the mighty dominance of this technology.
To simplify the complexities of NLP, Analytics Insight brings the top 10 natural language processing trends for 2021-
The application of both supervised and unsupervised learning gives monumental support to natural language processing. Take for instance, text analytics leverages unsupervised and supervised learning to understand technical terms in a document and their parts of speech while unsupervised learning can determine symbiotic relationships between them.
While reinforcement learning has improved considerably in terms of sample efficiency, training times, and overall best practices, training RL models from scratch is still comparatively very slow and unstable. Therefore, rather than training a model from scratch, data specialists will look forward to first train NLP based supervised models and then fine-tune it using Reinforcement learning.
Deep Learning's application to natural language processing is multifaceted. Techniques like Recurrent Neural Networks (RNN's) can give data scientists an accurate text classification by using parsing. Thus, RNNs would be a popular trend in certain text analytics platforms for document classification and entity tagging.
NLP would play a key role in tracking and monitoring market intelligence reports to extract intelligent information for businesses for future strategy formulation. 2021 and beyond NLP would find its application in a plethora of business domains. Currently this technology is widely used in financial marketing. It shares exhaustive insights into market sentiments, tender delays, and closings, and extracts information from large repositories.
Transfer learning will make way for pre-trained models creating applications for sentiment analysis, text classifications, and so on. In medical use cases, transfer learning will allow instances like patient satisfaction to be accurately measured. The same can be applied to any service industry where the satisfaction levels will be a score representing the likelihood if a consumer is satisfied or not.
E-retailers would use NLP and machine learning techniques to increase customer engagement, analyse their browsing patterns and shopping trends. Other intelligence insights include purchase behaviour, autogenerated product descriptions and so on.
The requirement for a semantic search is another trend anticipated to impact NLP in 2021. This search would engage both Natural Language Processing and Natural Language Understanding requiring a granular comprehension of the central ideas contained within the text.
NLP will also become more common in use cases that understand user intent like intelligent chatbots, and semantic search. Instigated by uses of deep learning, unsupervised and supervised machine learning, the plethora of natural language technologies will continue to mould the communication capacity of cognitive computing.
Powered by development in Natural Language Processing (NLP), the growth in chatbot and virtual assistant market would be robust. The chatbot market which was worth $2.6 billion in 2019 and is predicted to reach to US$9.4 billion by 2024.
Natural Language Processing would be a great tool to comprehend and analyse the audience responses to a brand communication published on social media platforms. Also known as opinion mining, it helps to analyse the attitude and emotional state (happy, sad, angry, annoyed, etc) of the consumer who is commenting/engaging with the company through social media posts.
The pragmatic use of NLP enables organizations with large amounts of unstructured text or spoken data to overcome dark data issues and effectively mine them for insight. What's truly notable about NLP however, is the multiple dimensions of AI it involves, hinting at the overall dynamic impact this technology will have in the coming years.
