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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 641–650 of 753 papers

TitleStatusHype
Learning Paraphrasing for Multiword Expressions—0
Towards Constructing Sports News from Live Text Commentary—0
News Citation Recommendation with Implicit and Explicit Semantics—0
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents—0
Using Learning-To-Rank to Enhance NLM Medical Text Indexer Results—0
Learning Optimal Card Ranking from Query Reformulation—0
Learning Term Weights for Ad-hoc Retrieval—0
Recognizing Reference Spans and Classifying their Discourse Facets—0
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking—0
QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding—0
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