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ACL
2011
14 years 4 months ago
Learning Word Vectors for Sentiment Analysis
Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and...
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Da...
ECML
2006
Springer
15 years 4 months ago
An Efficient Approximation to Lookahead in Relational Learners
Abstract. Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search mis...
Jan Struyf, Jesse Davis, C. David Page Jr.
84
Voted
KDD
1994
ACM
140views Data Mining» more  KDD 1994»
15 years 5 months ago
A Comparison of Pruning Methods for Relational Concept Learning
Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are m...
Johannes Fürnkranz
CORR
1999
Springer
112views Education» more  CORR 1999»
15 years 25 days ago
Learning Transformation Rules to Find Grammatical Relations
Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transformation sequences and-e...
Lisa Ferro, Marc B. Vilain, Alexander S. Yeh
ICCV
2001
IEEE
16 years 3 months ago
Learning the Semantics of Words and Pictures
We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features...
Kobus Barnard, David A. Forsyth