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» Learning to rank on graphs
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ICWSM
2009
14 years 7 months ago
Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations
User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their...
Jason S. Kessler, Nicolas Nicolov
ML
2010
ACM
141views Machine Learning» more  ML 2010»
14 years 8 months ago
Relational retrieval using a combination of path-constrained random walks
Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval o...
Ni Lao, William W. Cohen
EMNLP
2011
13 years 9 months ago
Random Walk Inference and Learning in A Large Scale Knowledge Base
We consider the problem of performing learning and inference in a large scale knowledge base containing imperfect knowledge with incomplete coverage. We show that a soft inference...
Ni Lao, Tom M. Mitchell, William W. Cohen
138
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SIGIR
2011
ACM
14 years 13 days ago
Learning search tasks in queries and web pages via graph regularization
As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that ...
Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He,...
IPMU
2010
Springer
14 years 7 months ago
Rank Correlation Coefficient Correction by Removing Worst Cases
Abstract. Rank correlation can be used to compare two linearly ordered rankings. If the rankings include noise values, the rank correlation coefficient will yield lower values than...
Martin Krone, Frank Klawonn