TREC 2025 Proceedings

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 over title + full text. Software/config: PyTerrier 0.10.0, Terrier 5.11, terrier-prf plugin for RM3; parse=false. Query processing: Parser-safe normalization, followed by hedge/uncertainty removal using a fixed lexicon (data/hedges.txt). Removal is case-insensitive, phrase-level, longest-first; only hedge phrases are deleted and content words are preserved. Negations: No negation detection or penalties are applied in this run. Retrieval (pseudo-relevance feedback): Two-stage PRF pipeline on the hedges-removed query: BM25 initial retrieval with feedback depth 50. RM3 with fb_docs=10, fb_terms=20 to build an expansion. BM25 final retrieval to return 1000 documents per query. Ranking/output: Sort by score; enforce exactly 1000 docs per query; TREC format with run_id rm3_hedges. External resources/baselines: No LLMs or 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

• rm3_hedges.trec_eval (trec_eval)

Paper