Text Mining, Information and Fact Extraction (TMIFE)

author: Marie-Francine Moens, Department of Computer Science, KU Leuven
published: Nov. 4, 2008,   recorded: September 2008,   views: 16462


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 1:25:34
Watch Part 2
Part 2 1:21:47
Watch Part 3
Part 3 1:32:56
Watch Part 4
Part 4 1:46:16
Watch Part 5
Part 5 1:38:13


communities (medical informatics, security, blog and news analysis, business information analysis, legal informatics, etc.). ?Still, today it is a somewhat fragmented subfield of human language technologies and information retrieval where the themes of (often forgotten) old-style pattern-based IE and more recent machine learning techniques, as applied in medical informatics, opinion mining and blog extraction, are scattered in various conferences and sessions (computational linguistics, artificial intelligence, machine learning, Web technologies, semantic computing). The aim of this tutorial is to explain important technologies from handcrafted patterns to learning, and especially focus on how they blend together in order to suit the needs of current information systems that retrieve or mine information, or that make decisions and solve problems based on the extracted information. This unified perspective also entails valuable insights into the role of traditional pipelined system architectures and more recent probabilistic inference techniques. Probabilistic extraction, by which text is translated into a variety of semantic labels, pe"../slides/rfectly integrates with probabilistic retrieval models that naturally combine surface text features and semantic labels in ranking computations, among which are the popular language retrieval models. Finally, information extraction alleviates the knowledge acquisition bottleneck in expert and question answering systems technology that operate in more restricted subject domains. We conclude with some pointers to new challenges among which are the recognition of complex semantic concepts (e.g., narrative scripts, or issues such as medical malpractice or competitiveness) in texts. Because of the reconciling aspects of the many techniques and application domains, the tutorial will attract students and researchers with different backgrounds.

See Also:

Download slides icon Download slides: russir08_moens_tmife_01.pdf (522.0 KB)

Download slides icon Download slides: russir08_moens_tmife_01.ppt (828.0 KB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Anwer, February 7, 2015 at 8:40 a.m.:

Very useful lecture

Write your own review or comment:

make sure you have javascript enabled or clear this field: