Implemented five retrieval models using elasticsearch and compared their performances.
This project uses trec_ap89 corpus that can be found from TREC.
- Use Jsoup to read the HTML like formated files from the dataset.
- Parse them to json format by google-gson.
- Use HTTP client to upload json files to a local ElasticSearch index.
Implement document ranking by retrieving information such as TF and DF from the local ElasticSearch index in five retrieval models:
- Vector space models:
Okapi-TFandTF-IDF. - Language models:
Okapi BM25, Unigram LM withLaplacesmoothing and Unigram LM withJelinek-Mercersmoothing.
- For each query, generate top 1000 documents by the ranking models and same them as result files.
- Run evaluation in command line for each result file using trec_eval and qrel_file to get the following results.
trec_eval [-q] qrel_file results_file
| Model | Average Precision | At 10 docs | At 30 docs |
|---|---|---|---|
| Okapi TF | 0.1828 | 0.4000 | 0.2693 |
| TF-IDF | 0.2547 | 0.3720 | 0.3253 |
| Okapi BM25 | 0.2631 | 0.4160 | 0.3307 |
| Laplace | 0.1749 | 0.3560 | 0.2720 |
| Jelinek-Mercer | 0.1984 | 0.3120 | 0.2627 |
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Copyright (c) 2017 Yixing Zhang
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