Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques

By:

Kulkarni, Akshay

Contributor(s):

Shivananda, Adarsha

Publication details: Apress 2024Description: xvii, 317pISBN:

9781484285237

Subject(s):

DDC classification:

006.35/KUL/SHI

Summary: The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space.

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Holdings
Item type	Current library	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Data Mining	006.35/KUL/SHI (Browse shelf(Opens below))	Available	Implement full-fledged intelligent NLP applications with Python, Translate real-world business problem on text data with NLP techniques, Leverage machine learning and deep learning techniques to perform smart language processing, Gain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification, and more.		SICSR-B-19667

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The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space.

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