The global natural language processing market has witnessed an impressive growth over the years with a valuation of USD 10.72 billion in 2020. The growth is expected to increase furthermore by 2026 to 48.46 billion USD. This makes the growth rate of the market to a massive 26.84%.
Natural Language Processing (NLP) is a subset of Artificial Intelligence which is now one of the most desired topics for research in the field of Science, Technology, Engineering and Mathematics (STEM). This area is still under development , many giant players in the tech market are in the process of developing and understanding the demand of the customers to infuse NLP in the services.
Let’s take a look at some of the scenarios of NLP which are trending in the field of research and development:
- Question Answering : Chatbots, information retrieval and dialog systems are some of the most used applications in this domain. Automation is on the run and this application serves the best. The algorithms and pre-structured database helps the program talk to humans in a natural language and tone and answer their queries. One of the best models in this are: BiDAF, BERT, and XLNet.
- Text Classification : This technique is capable of categorizing and classifying texts and then puts them in certain groups based on the analysis. This technique is quite useful in getting a comparative assessment of specific information in different languages. Some of the best models in this segment are : BERT, XLNet, and RoBERTa.
- Text summarization : This method is considered to be one of the most accurate ones in interpreting textual data. Text summarization has two main subsets, the first one extractive summarization and the second one is abstractive summarization. In extractive summarization, the sentences are ranked on a high or low basis which are taken from the provided data and then optimizes the sentences in order to make an analytical summary. While in the abstractive summarization , the main aim is to interpret the given data and in return make them understandable in any language. Some of the best models are BERTSumExt, BERTSumAbs, and UniLM (s2s-ft).
- Sentiment Analysis : Understanding human emotions and sentiments is really essential for any organization. Sentiment Analysis technique has the ability to assess human emotions and then categorizes them via its text analysis methods. With the rapid growth of social media domains such as Facebook, Instagram and many more, these techniques are being used on a wider scale for brand monitoring, market research, customer service and many such requirements. Some of the best models are : Dependency Parser, BERT, and RoBERTa.
- Speech Recognition : This technique is widely used and trusted for its accuracy and efficiency. Speech recognition implements spoken words and speeches and renders it into machine language. Many tech giants like Google and Amazon use this model in their projects. Some of the models are : BERT, RoBERTa, etc.
Growth in Natural Language Processing based projects has made human work much more efficient and accurate. The processes are now becoming more and more automated with massive research and development taking place in this field. With growth in research and development in NLP, it will result in more cost reduction and a better overall customer experience. With more efficient and accurate models, it will attract more companies for a better investment in this field which will impact the economic growth of the field of artificial intelligence and NLP.
Author- Toshank Bhardwaj, AI Content Creator
