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Enhancing MRR and Document Retrieval Techniques: Insights from TREC Evaluations

This report details our journey to improve Mean Reciprocal Rank (MRR) through various enhancements. After implementing a custom Lucene analyzer and modifying our indexing approach, we achieved a notable MRR increase from 0.300 to 0.321 during TREC 2004. Enhanced query expansion strategies and NE boosting further improved performance metrics. While answer extraction remains a challenge, ongoing refinements promise better results. Key methodologies include using stemming, stop-word filtering, and advanced term-weight techniques to ensure more accurate retrieval in subsequent TREC evaluations.

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Enhancing MRR and Document Retrieval Techniques: Insights from TREC Evaluations

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  1. LING 573D4: The Final D Elliot Holt Kelly Peterson

  2. D4 – Smells Like D3 • Primary Goal – improve D3 MAP with lessons learned • After many experiments: • TREC 2004 MAP = 0.300 -> 0.321 (+7%) • Global MRR++ • Help came from: • Custom Lucene Analyzer • Porter stem the index • NLTK stop the index • Added <HEADLINE> to index • (NOTE: not helpful for QA)

  3. Answer Extraction? • Not yet… Still improving the input pipes • Spillover splitting/scoring • SiteQPR System MRR Improvements • 3sent window = 0.329 • 1000 start = 0.403350241221 • 1000 baseline = 0.41155987459 • 1000 final = 0.424

  4. Answer Extraction? • Hahaha. No. • Enhanced Query Expansion - IT WORKS! • IT: • using improved Rocchio/expanded term weights • using expanded query terms for scoring initial documents • researching for better documents • WORKS: • SignificantMRR improvements • 2004: • [1] 100: 0.227 -> 0.232 (+2.2%) • [0-3] 1000: 0.398 -> 0.424 (+6.5%) • 2005 • [1-2] 1000: 0.392 -> 0.398 (+1.5%)

  5. Answer Extraction? • Well, kinda… • Development Efforts: • Hand-built Pattern matching reranking boost • (Soubbotin 2001) • NE boosting with Q_Class associations • (Echihabi et al, 2005) • Needed accuracy evaluation method • Create a corrected TREC 2004 Q_Class label file…manually • Extract information from compute_mrr.py results • TREC 2004 data, 100 char, average MRR • Miss = question with 0.0 MRR • HitAccuracy = Average MRR of non-miss questions

  6. By-Label Accuracy 1000 char: (lenient) 100 char: (lenient)

  7. Answer Extraction Results • Similar or better results without • The systems are implemented internally, but untuned • Worst performers (that could have been helped): • by Misses: • NUM:date • HUM:indiv • By HitAccuracy: • NUM:money • LOC • More patterns & NE associations needed

  8. TREC 2004 tuning results

  9. TREC 2005 testing results

  10. Further work • Apply new pattern and NE boosting to disaster areas. • Gloat about getting query expansion to improve both document and passage retrieval. • Restart.

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