Paper Info
Title
LIMSI: learning semantic similarity by selecting random word subsets
Abstract
We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for the semantics modeled. Despite ignoring text preprocessing and dispensing with semantic resources, the approach was ranked as high as 22nd among 89 participants in the SemEval-2012 Task6: Semantic Textual Similarity.
Year
Venue
Keywords
2012
SemEval@NAACL-HLT
semantic similarity,context vector feature,semantic textual similarity,text preprocessing,random word subsets,random indexing,classical ri,semeval-2012 task6,semantic resource
Field
DocType
Citations 
Semantic similarity,Random indexing,Information retrieval,Ranking,Computer science,Preprocessor,Natural language processing,Artificial intelligence,Boosting (machine learning),Semantic computing,Semantics,Semantic compression
Conference
2
PageRank 
References 
Authors
0.39
8
1
Name
Order
Citations
PageRank
Artem Sokolov115316.08