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WSM Project: Ranking by Vector Space Models

WSM Project

This project search for top 10 relevant articles based on input queries among 7,034 English news collected from reuters.com. The retrieved results will be shown as NewsID \t Similarity Score, and these results will be ranked from top relevant to least relevant.

Example Output

TF Weighting + Cosine Similarity
News            Score
---------       ---------
News123256      0.48507125007266594
News119356      0.48507125007266594
News108578      0.4472135954999579
News120265      0.4472135954999579
News103117      0.38575837490522974
News101763      0.3768891807222045
News111959      0.3768891807222045
News115859      0.3768891807222045
News119746      0.3768891807222045
News122919      0.37267799624996495
-------------------------

Execute Directly

$ python3 main.py --query '<querylist>'

Command Format

$ python3 main.py -d <doc_list_folder> -w <weight_type> -r <relevance_type> -q '<querylist>' [-f]
  • -d, --doc: Specify search target folder.
  • -w, --weight: Specify weight type, for instance: tf or tfidf.
  • -r, --relevance: Define how to measure similarity, cs stands for Cosine Similarity, eu stands for Euclidean Distance.
  • -q, --query: Define searching query, words seperated by (space).
  • -f, --feedback: add this argument if want to calculate pseudo feedback.

Command Example

Vector Space Model with Different Weighting Schemes & Similarity Metrics

  1. Term Frequency (TF) Weighting + Cosine Similarity
    $ python3 main.py -d EnglishNews -w tf -r cs -q 'trump biden taiwan china'
  2. Term Frequency (TF) Weighting + Euclidean Distance
    $ python3 main.py -d EnglishNews -w tf -r eu -q 'trump biden taiwan china'
  3. TF-IDF Weighting + Cosine Similarity
    $ python3 main.py -d EnglishNews -w tfidf -r cs -q 'trump biden taiwan china'
  4. TF-IDF Weighting + Euclidean Distance
    $ python3 main.py -d EnglishNews -w tfidf -r eu -q 'trump biden taiwan china'

Relevance Feedback

$ python3 main.py -d EnglishNews -w tfidf -r cs -q 'trump biden taiwan china' -f