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Natural Language Understanding with Python

Natural Language Understanding with Python

By : Deborah A. Dahl
4.8 (13)
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Natural Language Understanding with Python

Natural Language Understanding with Python

4.8 (13)
By: Deborah A. Dahl

Overview of this book

Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications. with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future. By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.
Table of Contents (21 chapters)
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1
Part 1: Getting Started with Natural Language Understanding Technology
4
Part 2:Developing and Testing Natural Language Understanding Systems
16
Part 3: Systems in Action – Applying Natural Language Understanding at Scale

Representing words with context-dependent vectors

Word2Vec’s word vectors are context-independent in that a word always has the same vector no matter what context it occurs in. However, in fact, the meanings of words are strongly affected by nearby words. For example, the meanings of the word film in We enjoyed the film and the table was covered with a thin film of dust are quite different. To capture these contextual differences in meanings, we would like to have a way to have different vector representations of these words that reflect the differences in meanings that result from the different contexts. This research direction has been extensively explored in the last few years, starting with the BERT (Bidirectional Encoder Representations from Transformers) system (https://aclanthology.org/N19-1423/ (Devlin et al., NAACL 2019)).

This approach has resulted in great improvements in NLP technology, which we will want to discuss in depth. For that reason, we will postpone...

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