The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification
By Jimmy J. Lin. 2019
12 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jimmy J. Lin
- Rodrigo Nogueira
- Ronak Pradeep
- Kyunghyun Cho
- Jamie Callan
- Zeynep Akkalyoncu Yilmaz
- Sebastian Hofstätter
- Yue Zhang
In Computer Vision and Pattern Recognition
- Jimmy J. Lin
- Rodrigo Nogueira
- Kyunghyun Cho
- Ronak Pradeep
- Raphael Tang
- Yue Zhang
- Nikhil Gupta
- Hui Fang
Citations in the corpus
- Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset (2020, methodology): “Instead, we implemented a robust approach that treats relevance feedback as a document clas-sification problem using simple linear classifiers, described in Yu et al. (2019) and Lin (2019).”