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» Extracting Important Sentences with Support Vector Machines
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BMCBI
2010
159views more  BMCBI 2010»
13 years 7 months ago
Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines
Background: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods ha...
Alvaro J. González, Li Liao
BMCBI
2005
107views more  BMCBI 2005»
13 years 7 months ago
Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multip
Background: Predicting the subcellular localization of proteins is important for determining the function of proteins. Previous works focused on predicting protein localization in...
Jiren Wang, Wing-Kin Sung, Arun Krishnan, Kuo-Bin ...
ERCIMDL
2010
Springer
180views Education» more  ERCIMDL 2010»
13 years 4 months ago
SciPlore Xtract: Extracting Titles from Scientific PDF Documents by Analyzing Style Information (Font Size)
Extracting titles from a PDFs full text is an important task in information retrieval to identify PDFs. Existing approaches apply complicated and expensive (in terms of calculating...
Jöran Beel, Bela Gipp, Ammar Shaker, Nick Fri...
ICML
2007
IEEE
14 years 8 months ago
Fast and effective kernels for relational learning from texts
In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such mode...
Alessandro Moschitti, Fabio Massimo Zanzotto
ACL
2004
13 years 9 months ago
Dependency Tree Kernels for Relation Extraction
We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and clas...
Aron Culotta, Jeffrey S. Sorensen