# The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification

arXiv: https://arxiv.org/abs/1904.08861 · HTML: https://mlsanity.com/paper/1904.08861/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://mlsanity.com/paper/2105.12936/) (2021)
- [Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking](https://mlsanity.com/paper/2210.10695/) (2022)
- [Online Distillation for Pseudo-Relevance Feedback](https://mlsanity.com/paper/2306.09657/) (2023)

## Abstract (arXiv)

Motivated by recent commentary that has questioned today's pursuit of ever-more complex models and mathematical formalisms in applied machine learning and whether meaningful empirical progress is actually being made, this paper tries to tackle the decades-old problem of pseudo-relevance feedback with "the simplest thing that can possibly work". I present a technique based on training a document relevance classifier for each information need using pseudo-labels from an initial ranked list and then applying the classifier to rerank the retrieved documents. Experiments demonstrate significant improvements across a number of newswire collections, with initial rankings supplied by "bag of words" BM25 as well as from a well-tuned query expansion model. While this simple technique draws elements from several well-known threads in the literature, to my knowledge this exact combination has not previously been proposed and evaluated.