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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

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Showing 671–680 of 753 papers

Title	Date	Tasks	Status
Stochastic Top-k ListNet	Nov 1, 2015	Learning-To-Rank	—Unverified
More Accurate Question Answering on Freebase	Oct 1, 2015	Learning-To-RankQuestion Answering	CodeCode Available
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering forWAT 2015	Oct 1, 2015	Chinese Word SegmentationDependency Parsing	—Unverified
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus	Sep 1, 2015	Learning-To-RankText Generation	—Unverified
BEER 1.1: ILLC UvA submission to metrics and tuning task	Sep 1, 2015	Learning-To-RankMachine Translation	CodeCode Available
LDTM: A Latent Document Type Model for Cumulative Citation Recommendation	Sep 1, 2015	Citation RecommendationInformation Retrieval	—Unverified
Learning Modulo Theories for preference elicitation in hybrid domains	Aug 18, 2015	Learning-To-Rank	—Unverified
Perceptron like Algorithms for Online Learning to Rank	Aug 4, 2015	General ClassificationInformation Retrieval	—Unverified
Challenges in clinical natural language processing for automated disorder normalization	Jul 14, 2015	Learning-To-RankMedical Named Entity Recognition	—Unverified
FAQ-based Question Answering via Word Alignment	Jul 9, 2015	Learning-To-RankQuestion Answering	—Unverified

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