International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
www.irjet.net
p-ISSN: 2395-0072
ADVANCEMENTS IN NLP AI: EXPLORING THE APPLICATIONS OF NAMED ENTITY RECOGNITION AND CONTEXTUAL LANGUAGE MODELS Gayathri Shivaraj Amazon, USA -----------------------------------------------------------------------***-----------------------------------------------------------------------ABSTRACT Artificial Intelligence (AI) advancements in Natural Language Processing (NLP) have completely changed how humans interact with machines and handle textual data. The introduction of touchless and voice-enabled applications has caused a paradigm shift in favor of more effective and customized communication. This study examines how NLP AI is revolutionizing a number of fields, such as sentiment analysis, named entity recognition (NER), and conversational AI.
Customer support services have seen a notable increase in the use of Conversational AI, which is driven by Machine Learning (ML) algorithms. Virtual assistants and chatbots are being used by businesses more frequently to improve customer satisfaction, streamline HR, and offer round-the-clock assistance [1]. These AI-powered systems use natural language processing (NLP) techniques to comprehend and reply to natural language queries, thereby facilitating communication between humans and machines. An essential NLP tool is sentiment analysis, which allows text data to be analyzed to ascertain the expressed sentiment (positive, negative, or neutral). This technology has shown to be extremely useful in market research, customer feedback analysis, brand reputation management, and social media monitoring. Businesses can obtain insightful knowledge about customer sentiment and make wise decisions by utilizing sentiment analysis. Information extraction, document summarization, and entity linking rely heavily on Named Entity Recognition (NER) systems. From unstructured text data, NER algorithms recognize and extract named entities, such as names of individuals, groups, places, dates, and numerical expressions [2]. This technology, which makes efficient information retrieval and knowledge discovery possible, has applications in a variety of fields, such as news analysis, legal document processing, and biomedical text mining. Furthermore, NLP AI has reached unprecedented heights thanks to recent developments in contextual language models, deep learning, and transfer learning. With the help of methods like BERT [3] and GPT [4], text understanding and generation capabilities have greatly improved, allowing for more precise and contextaware language processing. This paper provides a thorough overview of the field's rapid evolution and its profound impact on various industries and research domains. It dives into the principles, applications, and future directions of NLP AI innovations.
© 2024, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 1767