The TREC 2002 Filtering Track Report

By Stephen E. Robertson, Ian Soboroff. 2002

255 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Pretrained Transformers for Text Ranking: BERT and Beyond (2020, methodology): “Before that, document filtering was explored in the context of the TREC Filtering Tracks, which ran from 1995 [Lewis, 1995] to 2002 [Robertson and Soboroff, 2002], and the general research area of topic detection and tracking, also known as TDT [Allan, 2002].”
Evaluating sentence-level relevance feedback for high-recall information retrieval (2018, methodology): “A prior implementation of the same method had been shown to surpass the effectiveness of the ISJ results of (Cormack and Grossman, 2015) on the TREC 6 data shown in Figure 1, as well as a similar method independently contrived and used successfully by Soboroff and Robertson (2003) to construct relev”
Information Retrieval: A Biomedical and Health Perspective (2020, background): “All of these problems lead to what some call “waste” of research questions not being relevant to patients or physicians, design and methods not appropriate, reports never published, and some that are published being biased or not usable [273, 274].”
Feature-based transfer learning with real-world applications (2010, background): “Harpale and Yang [75] proposed an active learning framework for the multi-task adaptive filtering [157] problem.”
Overview of the TREC-2012 Microblog Track (2014, methodology): “The filtering task used the TREC filtering measures [4].”
Overview of the TREC 2016 Real-Time Summarization Track (2016, methodology): “Note that this metric is the same as the linear utility metric used in the TREC Filtering Tracks [4, 9], although our formulation is slightly diﬀerent.”
Effective Pattern Discovery for Text Mining (2012, methodology): “The most important information revealed in this table is that our proposed PTM (IPE) outperforms not only the pattern mining-based methods, but also the term-based methods including the state-of-the-art methods BM25 and SVM. PTM (IPE) is also outperforms CBM Pattern Matching and CBM in the ﬁve measu”
Active Multitask Learning Using Both Latent and Supervised Shared Topics (2014, background): “Harpale & Young [15] proposed active multitask learning for adaptive filtering [32] where the underlying classifier is logistic regression with Dirichlet process priors.”