1 / 10

# IR Homework #2 - PowerPoint PPT Presentation

IR Homework #2. By J. H. Wang Mar. 25, 2008. Programming Exercise #2: Term Weighting. Goal: to assign TF-IDF weights for each index term in inverted files Input : inverted index files (the output of HW#1) Output : term weighting files (exact format to be described later).

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' IR Homework #2' - raven-weaver

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### IR Homework #2

By J. H. Wang

Mar. 25, 2008

Programming Exercise #2: Term Weighting

• Goal: to assign TF-IDF weights for each index term in inverted files

• Input: inverted index files

• (the output of HW#1)

• Output: term weighting files

• (exact format to be described later)

• Two files

• Vocabulary file: a sorted list of words (each word in a separate line)

• Occurrences file: for each word, a list of occurrences in the original text

• [word#] [term freq.] [ (doc#, char#) pairs]

• 1 7 (1, 12) (1, 28) (3, 31) (8, 39) (8, 65) (10, 16) (11, 91)

• 2 2 (3, 44) (8, 72)

• Term-document matrix (N*M)

• Each row i contains the TF-IDF term weights wij for term ti in document dj

• 0.3 0.7 0.0 0.2 0.9 0.0 0.0 0.1 0.1 0.9 0.0 0.4 0.1 0.0…

• N: # of terms, M: # of documents

• Ex: 20k words * 400 docs = 8M entries!  But many of them are 0’s!

• Sparse matrix  how to store them in an efficient way?

• wij = tfij * log (N/dfi)

• We only keep entries with nonzero tfij

• Similar to occurrences file

• For each word, a list of nonzero entries in the term-document matrix

• [word#] [doc freq.] [ (doc# (j), wij) pairs]

• 1 4 (1, 0.3) (2, 0.7) (4, 0.2) (5, 0.9)

• 2 5 (1, 0.1) (2, 0.1) (3, 0.9) (5, 0.4) (6, 0.1)

• You will need both TF (term frequency) and DF (document frequency) factors for each term

• You can calculate the term frequencies and document frequencies at the same time when you build the index

• That is, you can combine HW#2 into HW#1 if necessary

• You may want to remove stopwords to further reduce the number of rows in the matrix

• Optional functionalities

• Other weighting schemes, such as: probabilistic weighting

• Stopword removal

• Dimension reduction strategies, such as Latent Semantic Indexing (or SVD)

• They should be able to be turned off by a parameter trigger

• Your submission should include

• The source code (and optionally your executable file)

• A one-page description that includes the following

• Major features in your work (ex: high efficiency, low storage, able to deal with multiple formats, …)

• Major difficulties encountered

• Special requirements for execution environments (ex: Java Runtime Environment)

• The names and the responsible parts of each individual member should be clearly identified for team work

• Due: three weeks (Apr. 16, 2008)

• The TF-IDF weighting files generated by your program will be checked for correctness

• Optional features such as probabilistic weighting and latent semantic indexing will be considered as bonus

• You might be required to demo if the program submitted was unable to run by TA