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Combining Families of Information Retrieval Algorithms Using Metalearning
Survey of Text Mining
This paper describes some experiments which use meta-learning to combine families of information retrieval (IR) algorithms obtained by varying the normalizations and similarity functions. By meta-learning, we mean the following simple idea: a family of IR algorithms is applied to a corpus of documents in which relevance is known to produce a learning set. A machine learning algorithm is then applied to this data set to produce a classifier which combines the different IR algorithms. Indoi:10.1007/978-1-4757-4305-0_7 fatcat:3pa6qs5pyzbdpnmww6nnrp5nwe