Linear feature-based models for information retrieval

By Donald Metzler, W. Bruce Croft. 2007

419 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Information Retrieval & Search

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Citations in the corpus

End-to-End Neural Ad-hoc Ranking with Kernel Pooling (2017, background): “Feature-based learning to rank baselines include RankSVM2, a state-of-the-art pairwise ranker, and coordinate ascent [16] (CoorAscent3), a state-of-the-art listwise ranker.”
Word-Entity Duet Representations for Document Ranking (2017, methodology): “Baselines included standard word-based baselines: Indri’s language model ( Lm ), sequential dependency model ( SDM ), and two state-of-the-art learning to rank methods: RankSVM [10] and co-ordinate ascent ( Coor-Ascent ) [15].”
Discriminative Information Retrieval for Question Answering Sentence Selection (2017, methodology): “Our framework can then be viewed as an instance of linear feature-based IR, following Metzler and Croft (2007).”
Content-based visual search learned from social media (2012, methodology): “Bearing these in mind, we choose the coordinate ascent based optimization technique, exploited by Metzler and Croft [82] in the domain of document retrieval.”
Learning to efficiently rank (2010, methodology): “We used a simple optimization procedure that directly optimizes Meet : a coordinate-level ascent algorithm similar to the one that was originally proposed in [17].”
Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search (2018, neutral): “For ClueWeb09-B, we used state-of-the-art baselines from prior research [28] 3 . baselines include Indri’s language model ( Indri ), Galagowithsequentialdependencymodelqueries( Galago - SDM ), and learning-to-rank models: RankSVM and Coor-Ascent .”
Learning to Reweight Terms with Distributed Representations (2015, methodology): “For WSD , we implemented the model with coordinate ascent [11] to directly optimize MAP on the training queries for WSD .”
Methods for exploring and mining tables on Wikipedia (2013, methodology): “A trained linear ranking model (learned with Coordinate Ascent [25]) ranks these tables using four types of features described in Table 3.”
An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain (2012, background): “C OORDINATE A SCENT (CA) [6] is a linear listwise model where the scores of the query-document pairs are calculated as weighted combinations of the feature values.”
Seq2Slate: Re-ranking and Slate Optimization with RNNs (2018, methodology): “Using the generated training data, we train both our seq2slate model and baseline rankers from the RankLib package: AdaRank [Xu and Li, 2007], Coordinate Ascent [Metzler and Croft, 2007], LambdaMART [Wu et al.”
Quality versus efficiency in document scoring with learning-to-rank models (2016, neutral): “This technique can be applied to learn a linear combination of the features of query-document pairs ( Metzler & Croft, 2007 ) The same as GBRT Analogously to GBRT . λ -MART θ t , θ l , θ s The same as GBRT Analogously to GBRT .”
TweetCred: Real-Time Credibility Assessment of Content on Twitter (2014, methodology): “We experimented with various methods that are typically used for information retrieval tasks: Coordinate Ascent [13], AdaRank [20], RankBoost [5] and SVM-rank [11].”
Long Document Ranking with Query-Directed Sparse Transformer (2020, methodology): “The latent representations of trained models are then considered as features for a Coor-Ascent ranker for low-label datasets using five-fold crossvalidation (Dai and Callan, 2019; Dai et al., 2018) to rerank top-100 SDM retrieved results (Metzler and Croft, 2007).”
Explore-exploit in top-N recommender systems via Gaussian processes (2014, methodology): “In particular, we compare against two best performing algorithms from the RankLib module of the Lemur Toolkit software 3 (Coordinate Ascent (LTR-CA) [19] and Rank Boost (LTR-RB) [9]) • Random selection of lists.”
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval (2018, background): “Feature-based baselines include two learning to rank systems, RankSVM (Joachims, 2002) and coordinate ascent (Coor-Accent) (Metzler and Croft, 2006).”