DocumentCode :
2967665
Title :
SeaLab Advanced Information Retrieval
Author :
Sangiacomo, Fabio ; Leoncini, Alessio ; Decherchi, Sergio ; Gastaldo, Paolo ; Zunino, Rodolfo
Author_Institution :
SeaLab, Univ. of Genoa, Genoa, Italy
fYear :
2010
fDate :
22-24 Sept. 2010
Firstpage :
444
Lastpage :
445
Abstract :
Information Retrieval is a well established interdisciplinary topic in which machine learning, computational linguistic, computer programming and data mining merge together. SLAIR stands for Sea Lab Advanced Information Retrieval and is an efficient software architecture that embeds these issues in a unique framework. SLAIR is expandable both from the data format and algorithm point of view. A pluggable notion of distance between documents drives the subsequent clustering/classification machinery, moreover SLAIR is explicitly designed to manage large scale text mining problems. The demo will be focused on the versatility of the framework, the main goal is to show how the different metrics provided by SLAIR can enhance clustering/classification ability and eventually lead to different views of the underlying textual data.
Keywords :
data mining; information retrieval; learning (artificial intelligence); pattern clustering; software architecture; SeaLab advanced information retrieval; clustering-classification machinery; computational linguistic; computer programming; data format; data mining; documents drives; machine learning; software architecture; text mining; textual data; Calibration; Clustering algorithms; Databases; Kernel; Semantics; Training; Hybrid Metric; Kernel K-Means; Semantic Representation; Support Vector Machines; Text Clustering; WordNet;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Semantic Computing (ICSC), 2010 IEEE Fourth International Conference on
Conference_Location :
Pittsburgh, PA
Print_ISBN :
978-1-4244-7912-2
Electronic_ISBN :
978-0-7695-4154-9
Type :
conf
DOI :
10.1109/ICSC.2010.48
Filename :
5629072
Link To Document :
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