Information Retrieval and Text Mining
author:
Thomas Hofmann,
Brown University
Description
This four hour course will provide an overview of applications of machine learning and statistics to problems in information retrieval and text mining. More specifically, it will cover tasks like document categorization, concept-based information retrieval, question-answering, topic detection and document clustering, information extraction, and recommender systems. The emphasis is on showing how machine learning techniques can help to automatically organize content and to provide efficient access to information in textual form.
You might be experiencing some problems with Your Video player.
| Slides | |
| 0:01 | Machine Learning in Information Retrieval |
| 2:29 | Motivation & Overview |
| 2:39 | A Brief History of the Library |
| 3:23 | Digital Information Repositories |
| 4:23 | Vannevar Bush (1945) |
| 5:31 | Memex |
| 6:27 | Information Retrieval |
| 7:40 | Machine Learning & Information Retrieval |
| 9:06 | Overview |
| 9:41 | Ad Hoc Retrieval |
| 9:48 | Ad Hoc Retrieval |
| 10:59 | Vocabulary Mismatch Problem |
| 12:40 | Search as Statistical Inference |
| 15:41 | Estimation Problem |
| 16:52 | Language Model Paradigm of IR |
| 17:04 | Language Model Paradigm |
| 18:48 | Language Model Paradigm |
| 18:54 | Language Model Paradigm |
| 21:21 | Unigram Model |
| 23:50 | Naive Approach |
| 24:49 | Laplace-Lidstone Discounting |
| 26:35 | Smoothing by Interpolation |
| 28:13 | Absolute Discounting |
| 30:47 | Hierarchical Bayesian Estimation |
| 33:14 | Hierarchical Bayesian Estimation |
| 35:37 | Hierarchical Bayesian Estimation |
| 37:29 | Two-Stage Smoothing |
| 39:40 | Advanced Smoothing |
| 40:47 | Latent Variable Models in IR |
| 41:04 | Document-Term Matrix |
| 42:22 | A 100 Millionths of a Typical Document-term Matrix |
| 43:11 | Matrix Decomposition |
| 44:33 | Probabilistic Latent Semantic Analysis |
Lecture rating
| People found this lecture: | ||
| Worth seeing | ||
| because it is: | ||
| Valuable and informative | ||
| Well presented | ||
| Easily understandable | ||
| Acceptably recorded | ||
| You need to login to cast your vote. | ||
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.
Related content
Visitors who watched this lecture also watched...
1 comment
Watch videos: (click on thumbnail to launch)
Part 1
0:46:30
|
Part 2
0:46:23
|
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Excellent lectures. Contains a good amount of technical detail but also very good motivating examples which relate the details to real world problems. Really excellent presentation. Many thanks for posting this video, this site is an extremely useful resource.
Simply Superb...Excellant lectures which will be very usefull for all the researchers in this field... every student and researchers should watch this one...