01843 [upload 2017] Challenges in Natural Language Processing M. Bales, et Al., (cambridge, 1993)

The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others.

By by Mohammad Taher Pilehvar (Author), Jose Camacho-Collados (Author)
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP)

by Emily M Bender (Author), Alex Lascarides (Author), Graeme Hirst (Editor)
Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG).

Create Word Cloud App Using Streamlit | Sentiment Analysis | Speech to text | Spam Detection | Code Walkthrough

Create App Using Streamlit | Sentiment Analysis | Speech to text | Spam Detection

Learn over 15+ tools including TextBlob,NLTK,Spacy,Flair, for performing NLP Projects

Build Real World Applications of Natural Language Processing [COMPLETE PROJECT]