Search Results for "term"

tidytext brings tidy data principles to text mining by converting text into a tidy data frame format. It provides tools for tokenization, sentiment analysis, n‑gram creation, and term‑document matrices, enabling interoperability with dplyr, ggplot2, and other tidyverse workflows.

...It provides an API and tools for (inverted) indexing, storage and retrieval of large amounts of text, with (XML-based) handling of meta-data, tools for text categorisation, including, functionality for XML parsing, term set reduction (and basic keyword extraction), probabilistic classifier induction, sample classification tools, and evaluation modules, a suite of corpus management, curation and distributed access tools. If you use the tool please consider referencing it using the following article: Luz, S., & Sheehan, S. (2020). Methods and visualization tools for the analysis of medical, political and scientific concepts in Genealogies of Knowledge. ...

...Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids.

The TextProcessor Java package is a text processing toolkit, which provides some frequently used text processing functions such as stemming, removing stop-words, generating a term vocabulary, and calculating the term-doc frequency matrix. Basic topic mining models such as LDA and sparse NMF are also supported. The package can also generate feature files from a given text dataset with LDA and LIBSVM format for posterior procedures such as classification or clustering. The toolkit is also being extended for more advanced text analysis tasks based on natural language processing techniques.