NLP

NLP Fine Tuning In natural language processing, fine-tuning is an essential method, especially with contemporary deep learning models. Definition of Fine Tuning in NLP The act of...

Transfer Learning NLP In Natural Language Processing (NLP), transfer learning is an important approach that applies knowledge learnt from training a model on one task...

This article discusses the difference between deep learning and NLP (natural language processing), as well as common deep learning frameworks for NLP applications. Deep Learning...

Feature engineering is a crucial stage in Natural Language Processing (NLP) since it structures raw text data for machine learning algorithms. Most computers and...

This blog discusses machine learning in natural language processing (NLP) and covers topics such as what is machine learning, key concepts and techniques, applications...

Word Embedding NLP In Natural Language Processing, word embeddings are a feature learning technique that maps vocabulary words to vectors of real numbers. Known as...

The contrast between generative and discriminative models is important for NLP tasks like sequence prediction and parsing. The two categories of models are broken out...

Dependency grammar And Dependency parsing What is Dependency Grammar? One important framework for explaining the syntactic structure of sentences is dependency grammar. Dependency grammar does not...

Treebank NLP Treebanks are corpora with linguistic annotations that go beyond part-of-speech labelling and incorporate some form of syntactic analysis. These are sets of sentences,...

Standard Context-Free Grammars (CFGs) used in natural language processing, especially for statistical parsing, are significantly extended by Probabilistic Context-Free Grammars (PCFGs), also referred to...