Named entity recognition using AI-NLP
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2019, Vol 5, Issue 11
Abstract
Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP), which involves identifying and categorizing named entities in unstructured text data. In recent years, deep learning-based approaches such as Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) have shown impressive performance in NER. Furthermore, word embeddings have also become a popular method for representing words in NLP tasks. In this paper, we propose a comprehensive review of recent advances in NER using AI-NLP techniques, specifically LSTM, CRF, and word embeddings. We discuss the underlying principles of these techniques and their advantages in NER. We also present a comparative analysis of these approaches on benchmark datasets and highlight their strengths and weaknesses. Furthermore, we discuss some of the key challenges in NER, such as handling rare and unknown entities, and explore potential solutions using these techniques. Overall, this paper provides a comprehensive overview of NER using AI-NLP with LSTM, CRF, and word embeddings
Authors and Affiliations
Pakkurthi Sai Anudeep and Dr. K Sasikala
Keywords
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