TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 10: Text Mining

This class introduces basics of web mining and information retrieval including, for example, an introduction to the Vector Space Model and Text Mining. Guest Lecturer: Dr. Michael Granitzer Optional: Modeling the Internet and the Web: Probabilistic Methods and Algorithms, Pierre Baldi, Paolo Frasconi, Padhraic Smyth, Wiley, 2003 (Chapter 4, Text Analysis)

Changing text and background colours in Adobe Acrobat

A 4-minute video that shows how students with dyslexia or visual stress can change the text and background colours in Adobe Acrobat Reader to suit their needs.

How to highlight selected text on a PowerPoint slide

This short 7-minute video shows two methods which can be used to highlight selected words or phrases on a PowerPoint slide.

INFO2009 Text Tutorial - Accessibility

Maximizing Accessibility in Software - by Denis's Angels

INFO2009 Poster: Accessibility - Text Tutorial

Poster describing the text tutorial on accessibility, created by Denis's Angels for INFO2009.

Reference List: Accessibility Text Tutorial

List of references in Harvard format for the accessibility text tutorial created by Denis's Angels.

Full text library resources 2010-11

Finding journal articles from full text sources such as IEEEXplore, ACM and LNCS (Lecture Noters in Computer Science)

Full Text Library Resources for INFO1010

These slides support students in understanding how to respond to the challenge of: "I’ve been told not to use Google or Wikipedia to research my essay. What else is there?" The powerpoint guides students in how to identify high quality, up to date and relevant resources on the web that they can reliably draw upon for their academic assignments. The slides were created by the subject liaison librarian who supports the School of Electronics and Computer Science at the University of Southampton, Fiona Nichols.

Finding and replacing text in MS Word 2010

Finding and replacing text in an MS Word 2010 document can save time when you need to make corrections to a word or phrase throughout your file. For best viewing Download the video.

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.