Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
By Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti. 2006
79 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Julia Hockenmaier
- Micah Hodosh
- Peter Young
- Danilo Croce
- Roberto Basili
- Alessandro Moschitti
- Alexander I. Rudnicky
- Aasish Pappu
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Julia Hockenmaier
- Yi Yang
- Trevor Darrell
- Alan L. Yuille
- Micah Hodosh
- Li Fei-Fei
- Dacheng Tao
In Natural Language Processing
- Julia Hockenmaier
- Micah Hodosh
- Peter Young
- Roberto Basili
- Danilo Croce
- Alessandro Moschitti
- Alexander I. Rudnicky
- Aasish Pappu
Citations in the corpus
- Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, methodology): “We use our training data to estimate P (s), and follow Bloehdorn et al. (2006) in assigning each word w its most frequent (first) noun sense s w in WordNet 3.0.”
- Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases (2013, methodology): “( Bloehdorn et al., 2006) has proposed a semantic smoothing kernel based approach for text classification.”
- Syntactic/Semantic Structures for Textual Entailment Recognition (2010, background): “(Basili et al., 2006; Basili et al., 2005; Bloehdorn et al., 2006).”
- Structured Lexical Similarity via Convolution Kernels on Dependency Trees (2011, methodology): “For example, in (Bloehdorn et al., 2006), bag of hyponyms and hypernyms (up to a certain level of WordNet hierarchy) were used to build such vectors.”