The Simplest Thing That Can Possibly Work: Pseudo-Relevance Feedback Using Text Classification
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.
Code (0)
등록된 구현이 없습니다.
Tasks
General Classificationtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To …
RetrievalLearning What Not to Forget: Long-Horizon Agent Memory from a Few Kilobytes of Learning
Long-running language-model systems accumulate interaction history that outgrows the context window, so they must continually evict. When an eviction policy drops a load-bearing detail, for example an access token issued…
Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with weak labels is a long-lived ill-posed problem. Mainstream methods mainly focus on improving the quality of pseudo labels. In this report, we attempt to explore the potential o…
Pseudo LabelSegmentationSemantic SegmentationWeakly supervised Semantic Segmentation+1GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback
Query expansion with pseudo-relevance feedback (PRF) is a powerful approach to enhance the effectiveness in information retrieval. Recently, with the rapid advance of deep learning techniques, neural text generation has …
Information RetrievalRerankingRetrievalText GenerationDomain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling
Although neural information retrieval has witnessed great improvements, recent works showed that the generalization ability of dense retrieval models on target domains with different distributions is limited, which contr…
Domain AdaptationInformation RetrievalKnowledge DistillationRetrieval