Natural language processing: state of the art, current trends and challenges SpringerLink

Comet Artifacts lets you track and reproduce complex multi-experiment scenarios, reuse data points, and easily iterate on datasets. Everybody makes spelling mistakes, but for the majority of us, we can gauge what the word was actually meant to be. However, this is a major challenge for computers as they don’t have the same ability to infer what the word was actually meant to spell.

It involves a variety of techniques, such as text analysis, speech recognition, machine learning, and natural language generation. These techniques enable computers to recognize and respond to human language, making it possible for machines to interact with us in a more natural way. Technologies such as unsupervised learning, zero-shot learning, few-shot learning, meta-learning, and migration learning are all essentially attempts to solve the low-resource problem. NLP is unable to effectively deal with the lack of labelled data that may exist in the machine translation of minority languages, dialogue systems for specific domains, customer service systems, Q&A systems, and so on. Several companies in BI spaces are trying to get with the trend and trying hard to ensure that data becomes more friendly and easily accessible. But still there is a long way for this.BI will also make it easier to access as GUI is not needed.

NLP Challenges

Noah Chomsky, one of the first linguists of twelfth century that started syntactic theories, marked a unique position in the field of theoretical linguistics because he revolutionized the area of syntax (Chomsky, 1965) [23]. Further, Natural Language Generation (NLG) is the process of producing phrases, sentences and paragraphs that are meaningful from an internal representation. The first objective of this paper is to give insights of the various important terminologies of NLP and NLG. Instead of requiring humans to perform [newline]feature engineering, neural networks will “learn” the important [newline]features via representation learning.

One of the main challenges that ML developers face is the intensive compute requirements for building and training large-scale ML models. Indeed, training large language models (LLMs) consumes billions of input words and costs millions of dollars in computational resources. Language is complex and full of nuances, variations, and concepts that machines cannot easily understand.

Overcoming Common Challenges in Natural Language Processing

The world of natural language processing and computer vision continues to evolve daily. This interdisciplinary field automates the key elements of human vision systems using sensors, smart computers, and machine learning algorithms. Computer vision is the technical theory underlying artificial intelligence systems’ capability to view – and understand – their surroundings.

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