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» Learning to Rank Using an Ensemble of Lambda-Gradient Models
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ACL
2009
13 years 5 months ago
A Graph-based Semi-Supervised Learning for Question-Answering
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment sco...
Asli Çelikyilmaz, Marcus Thint, Zhiheng Hua...
KDD
2004
ACM
210views Data Mining» more  KDD 2004»
14 years 8 months ago
Probabilistic author-topic models for information discovery
We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic pro...
Mark Steyvers, Padhraic Smyth, Michal Rosen-Zvi, T...
WWW
2010
ACM
14 years 2 months ago
Factorizing personalized Markov chains for next-basket recommendation
Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn...
Steffen Rendle, Christoph Freudenthaler, Lars Schm...
DASFAA
2007
IEEE
139views Database» more  DASFAA 2007»
14 years 2 months ago
Self-tuning in Graph-Based Reference Disambiguation
Nowadays many data mining/analysis applications use the graph analysis techniques for decision making. Many of these techniques are based on the importance of relationships among t...
Rabia Nuray-Turan, Dmitri V. Kalashnikov, Sharad M...
EMNLP
2010
13 years 5 months ago
Unsupervised Parse Selection for HPSG
Parser disambiguation with precision grammars generally takes place via statistical ranking of the parse yield of the grammar using a supervised parse selection model. In the stan...
Rebecca Dridan, Timothy Baldwin