coursera课程Text Retrieval and Search Engines之Week 1 Overview

Week 1

On this page:

Instructional Activities

Below is a list of the activities and assignments available to you this week. Click on the name of each activity for more detailed instructions.

Activity	Due Date*	Estimated Time Required
Week 1 Video Lectures	Sunday, March 29 (Suggested)	3 hours
Programming Assignments Overview	Sunday, March 29 (Suggested)	~1 hour
Week 1 Quiz	Sunday, April 19	~ 0.5 hour

* All deadlines are at 11:55 PM Central Time (time zone conversion) unless otherwise noted.

Time

This module will last 7 days and should take approximately 5 hours of dedicated time to complete, with its readings and assignments.

Goals and Objectives

After you actively engage in the learning experiences in this module, you should be able to:

Explain some basic concepts in natural language processing and text information access.
Explain why text retrieval is often defined as a ranking problem.
Explain how the vector space retrieval model works.
Explain what TF-IDF weighting is and why TF transformation and document length normalization is necessary for the design of an effective ranking function.

Key Phrases/Concepts

Keep your eyes open for the following key terms or phrases as you complete the readings and interact with the lectures. These topics will help you better understand the content in this module.

Part-of-speech tagging; syntactic analysis; semantic analysis; ambiguity
“Bag of words” representation
Push, pull, querying, browsing
Probability Ranking Principle
Relevance
Vector Space Model
Term Frequency (TF)
Document Frequency (DF); Inverse Document Frequency (IDF)
TF Transformation
Pivoted length normalization
Dot product
BM25

Guiding Questions

Develop your answers to the following guiding questions while watching the video lectures throughout the week.

What does a computer have to do in order to understand a natural language sentence?
What is ambiguity?
Why is natural language processing (NLP) difficult for computers?
What is bag-of-words representation? Why do modern search engines use this simple representation of text?
What are the two modes of text information access? Which mode does a Web search engine such as Google support?
When is browsing more useful than querying to help a user find relevant information?
Why is a text retrieval task defined as a ranking task?
What is a retrieval model?
What are the two assumptions made by the Probability Ranking Principle?
What is the Vector Space Retrieval Model? How does it work?
How do we define the dimensions of the Vector Space Model?
What are some different ways to place a document as a vector in the vector space?
What is Term Frequency (TF)?
What is TF Transformation?
What is Document Frequency (DF)?
What is Inverse Document Frequency (IDF)?
What is TF-IDF Weighting?
Why do we need to penalize long documents in text retrieval?
What is pivoted document length normalization?
What are the main ideas behind the retrieval function BM25?

Readings and Resources

The following readings are optional:

N. J. Belkin and W. B. Croft. "Information filtering and information retrieval: Two sides of the same coin?" Commun. ACM 35, 12 (Dec. 1992): 29-38.
A. Singhal, C. Buckley, and M. Mitra. "Pivoted document length normalization." In Proceedings of ACM SIGIR 1996.

Video Lectures

Video Lecture	Lecture Notes	Transcript	Video Download	SRT Caption File	Forum
1.1 Natural Language Processing (00:21:05)			(35.5 MB)
1.2 Text Access (00:09:24)			(12.8 MB)
1.3 Text Retrieval Problem (00:26:18)			(36.7 MB)
1.4 Overview of Text Retrieval Methods (00:10:10)			(13.7 MB)
1.5 Vector Space Model: Basic Idea (00:09:44)			(13.0 MB)
1.6 Vector Space Model: Instantiation (00:17:30)			(23.1 MB)
1.7 Vector Space Model: Improved Instantiation (00:16:52)			(22.1 MB)
1.8 TF Transformation (00:18:56)			(12.7 MB)
1.9 Doc Length Normalization (00:18:56)			(25.6 MB)

Tips for Success

To do well this week, I recommend that you do the following:

Review the video lectures a number of times to gain a solid understanding of the key questions and concepts introduced this week.
When possible, provide tips and suggestions to your peers in this class. As a learning community, we can help each other learn and grow. One way of doing this is by helping to address the questions that your peers pose. By engaging with each other, we’ll all learn better.
It’s always a good idea to refer to the video lectures and reference them in your responses. When appropriate, critique the information presented.
Take notes while you watch the lectures for this week. By taking notes, you are interacting with the material and will find that it is easier to remember and to understand. With your notes, you’ll also find that it’s easier to complete your assignments. So, go ahead, do yourself a favor; take some notes!

Getting and Giving Help

You can get/give help via the following means:

Use the Learner Help Center to find information regarding specific technical problems. For example, technical problems would include error messages, difficulty submitting assignments, or problems with video playback. You can access the Help Center by clicking on theHelp Center link at the top right of any course page. If you cannot find an answer in the documentation, you can also report your problem to the Coursera staff by clicking on the Contact Us! link available on each topic's page within the Learner Help Center.
Use the Content Issues forum to report errors in lecture video content, assignment questions and answers, assignment grading, text and links on course pages, or the content of other course materials. University of Illinois staff and Community TAs will monitor this forum and respond to issues.

As a reminder, the instructor is not able to answer emails sent directly to his account. Rather, all questions should be reported as described above.

from: https://class.coursera.org/textretrieval-001/wiki/Week1Overview