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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).

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ir homework 2

IR Homework #2

By J. H. Wang

Mar. 25, 2008

programming exercise 2 term weighting
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
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
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
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
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 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
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
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
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