9 Natural Language Processing Trends in 2023

9 Natural Language Processing Trends in 2023

Natural language processing (NLP) is a subset of AI which finds growing importance due to the increasing amount of unstructured language data. The rapid growth of social media and digital data creates significant challenges in analyzing vast user data to generate insights. Further, interactive automation systems such as chatbots are unable to fully replace humans due to their lack of understanding of semantics and context. To tackle these issues, Natural language models are utilizing advanced Machine Learning (ML) to better understand unstructured voice and text data. This article provides an overview of the top global Natural Language Processing trends in 2023. They range from virtual agents and sentiment analysis to semantic search and reinforcement learning.

For this in-depth research on the Top Natural Language Processing Trends & Startups, we analyzed a sample of 1 645 global startups & scaleups. The result of this research is data-driven innovation intelligence that improves strategic decision-making by giving you an overview of emerging technologies & startups advancing data processing. These insights are derived by working with our Big Data & Artificial Intelligence-powered StartUs Insights Discovery Platform, covering 2 500 000+ startups & scaleups globally. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant startups, emerging technologies & future industry trends quickly & exhaustively.

In the Innovation Map below, you get an overview of the Top 9 Natural Language Processing Trends & Innovations that impact 1 645 companies worldwide. Moreover, the Natural Language Processing Innovation Map reveals 18 hand-picked startups, all working on emerging technologies that advance their field.

Based on the Natural Language Processing Innovation Map, the Tree Map below illustrates the impact of the Top 9 NLP Trends in 2023. Virtual assistants improve customer relationships and worker productivity through smarter assistance functions. Advances in learning models, such as reinforced and transfer learning, are reducing the time to train natural language processors. Besides, sentiment analysis and semantic search enable language processors to better understand text and speech context. Named entity recognition (NER) works to identify names and persons within unstructured data while text summarization reduces text volume to provide important key points. Language transformers are also advancing language processors through self-attention. Lastly, multilingual language models use Machine Learning to analyze text in multiple languages.

The Global Startup Heat Map below highlights the global distribution of the 1 645 exemplary startups & scaleups that we analyzed for this research. Created through the StartUs Insights Discovery Platform, the Heat Map reveals that the US sees the most startup activity.

Below, you get to meet 18 out of these 1 645 promising startups & scaleups as well as the solutions they develop. These natural language processing startups are hand-picked based on criteria such as founding year, location, funding raised, & more. Depending on your specific needs, your top picks might look entirely different.

There is a growing interest in virtual assistants in devices and applications as they improve accessibility and provide information on demand. However, they deliver accurate information only if the virtual assistants understand the query without misinterpretation. That is why startups are leveraging NLP to develop novel virtual assistants and chatbots. They mitigate processing errors and work continuously, unlike human virtual assistants. Additionally, NLP-powered virtual assistants find applications in providing information to factory workers, assisting academic research, and more.

Australian startup Servicely develops Sofi, an AI-powered self-service automation software solution.

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