IR Homework #2

By J. H. WangMar. 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)

Input: Inverted Index

• 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)• …

TF-IDF Weighting

• 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?

Output Format

• 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) • …

Implementation Issues

• 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 Features

• 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

Submission

• 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)

Evaluation

• 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

Questions?