It’s time for our final episode of this series of ‘Key concepts and areas in TDM explained’. This time Robert Patton of the Oak Ridge National Laboratories introduces Deep Learning and discusses how it can be applied in practice.
Knowledge discovery is the process of discovering new information. In text and data mining this happens for example by finding new connections or trends in a large amount of text and data. Ron Daniel is director at the Elsevier Labs. He explains Knowledge Discovery and Knowledge Representation in three short videos.
In the old days, if you would do a search in a search engine, you would get a lot of irrelevant hits that for some reason contained the keyword you used. Nowadays search engines give you much better results, because they put the keyword into context. This new way of searching is called ‘Semantic Search‘. Waleed Ammar of the Allen Intitute for Artificial Ingelligence explains semantic search, the challenges and the state-of-the-art in a few short video clips.
One of the things you can do with textmining, is discovering conceptually related items within a collection of text and data. Want to know more? Anas Alzogbi is research assistant and doctoral student at the University of Freiburg. He explains Recommenders and Filtering in four short movies.
It’s time for the second part of ‘Key concepts and areas in TDM explained’. This time, Jevin West tells us more about “Text and Data Mining” and “Knowledge Representation” in three short videos. Jevin West is Assistant Professor at the University of Washington and Co-ordinator of DataLab.
What are the benefits of text and data mining (TDM) and how can its practices be applied in science? We asked recognised experts in the field to introduce key areas and concepts in short videos. The videos will be released during the following weeks in a series of blogposts. Today we start with day 1: introduction to text and data mining. The videos will also be part of the TDM Knowledge Base .