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24 lines
1.6 KiB
Markdown
24 lines
1.6 KiB
Markdown
## How to use
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Run `python3 phrase_index.py` with any of the parameters listed below:
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```
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-h, --help show this help message and exit
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--db DB Database file name
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--dataset DATASET ir_datasets name (e.g., cranfield, msmarco-passage)
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--stopwords STOPWORDS Stopwords to use (english, none)
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--mode MODE Indexing mode (duckdb, phrases)
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--min-freq MIN_FREQ Minimum frequency for phrases (only for mode "phrases")
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--min-pmi MIN_PMI Minimum PMI for phrases (only for mode "phrases")
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```
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## Helper scripts
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- `./auto_phrase.sh` and `./auto_zoekeend.sh` can be used to automatically index, search and evaluate the results and store it in a results directory. `auto_phrase` uses `phrase_index.py`, while `auto_zoekeend` uses `ze_index.py`.
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- `./batch_phrase.sh` can be used to create the results using multiple different variables in one go.
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- And `display_results.sh` can be used to display the evaluation metrics of all previous results. (So MAP, CiP, dictionary size, terms size, number of phrases, AVGDL and SUMDF)
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### Statistical Analysis and Comparison
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- **[compare_phrases_vs_duckdb.py](compare_phrases_vs_duckdb.py)** - Performs two-tailed pairwise sign test comparing MAP (Mean Average Precision) results between phrase-based and baseline approaches. Uses min_pmi=24 as baseline. Requires scipy for statistical testing.
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- **[compare_postings_cost_vs_duckdb.py](compare_postings_cost_vs_duckdb.py)** - Similar to above but compares Cost in Postings (CiP) metric instead of MAP. Evaluates computational efficiency of different indexing approaches.
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