TREC 2025 Proceedings
bm25_hedge_aware
Submission Details
- Organization
- UAmsterdam
- Track
- Tip-of-the-Tongue Search
- Task
- Retrieval Task
- Date
- 2025-09-10
Run Description
- Please describe in details how this run was generated
- Corpus and index: TREC ToT 2025 Wikipedia JSONL; PyTerrier/Terrier index (title + full text), no field weighting changes.
Software: PyTerrier 0.10.0, Terrier 5.11. Queries executed with controls parse=false (bag-of-words).
Query normalization: strip punctuation that can trip Terrier (slashes, curly quotes, long dashes, ellipses), remove remaining non-alphanumerics, collapse whitespace. No manual per-query edits.
Hedges: removed via fixed list data/hedges.txt (case-insensitive phrase removal; removes only hedge/booster phrases such as “maybe”, “i’m not sure”, “kind of”, “definitely”; content words like names/years/places are kept).
Retrieval: BM25 first-stage only, num_results=1000, applied to the hedges-removed query.
Post-processing: enforce exactly 1000 docs per query; sort by score; TREC format with run_id bm25_hedges.
External resources: none; no official baseline runfiles used.
Run type: Automatic.
- Specify datasets used in this run.
- ['Other']
- (if you checked "other", describe here)
- None
- Are you 100% confident that no data from https://github.com/microsoft/Tip-of-the-Tongue-Known-Item-Retrieval-Dataset-for-Movie-Identification or iRememberThisMovie.com (besides the training data provided as part of this year's track) was used for producing this run (including any data used for pretraining models that you are building on top of)?
- Yes I am confident that no data from those sources except the official track training data was used to produce this run
- Did you use any of the official baseline runs in any way to produce this run?
- no
- If you did use any of the official baseline runs in any way to produce this run, please describe how below in sufficient detail (e.g., as reranking candidates or in ensemble with other approaches).
Evaluation Files
Paper