This bookpresents the creation of a bilingual thesaurus (Italian and English), and its conversion into an ontology system, oriented to the Cybersecurity field of knowledge term management and the identification of a replicable method over other specialized areas of study, throug…
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text dat…
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies or groceries, goes a long way in defining user experience and enticing your customers to use and buy …
Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured informa…
Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques …
Natural Language Processing with Java will explore how to automatically organize text using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. You will leverage the power of Java to extract relationships …
The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While …
Chapter 2: Data Exploration; An overview; Feature identification; The curse of dimensionality; Feature selection and feature extraction; Pearson correlation example; Cleaning and preparing data; Handling missing data; Missing categorical data; Missing numerical data; Handling no…