TREC 2025 Proceedings

Submission Details

Organization

DUTH

Track

Tip-of-the-Tongue Search

Task

Retrieval Task

Date

2025-09-03

Run Description

Please describe in details how this run was generated

Automatic lexical ensemble using PyTerrier/Terrier over the official TREC ToT 2025 Wikipedia corpus. Indexing: Terrier with Stopwords + PorterStemmer, EnglishTokeniser, and blocks (positions). Document text = title + body (whitespace normalized). Query processing: remove control characters and punctuation, normalize spaces, keep ≤128 tokens (Terrier truncates long queries internally). Retrieval pipelines: BM25, PL2, and DPH (document-level) plus pseudo-relevance feedback with RM3 (fb_terms=50, fb_docs=20, lambda=0.6) on top of BM25. Fusion: RRF (k=60) across base lexical and RM3 branches. Output: top-1000 Wikipedia page ids per query. No manual intervention on test; parameters chosen a-priori/validated only on the provided dev splits.

Specify datasets used in this run.

["This year's TREC TOT training data"]

(if you checked "other", describe here)

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).

None. We did not use any official baseline run files. The run was produced solely by our own PyTerrier/Terrier pipelines over the official TREC ToT 2025 corpus; no baseline runs were reused, re-ranked, or fused.

Evaluation Files

• lex-stronger-testv2.trec_eval (trec_eval)

Paper