DocumentCode :
3315469
Title :
Neural Networks for Author Attribution
Author :
Tsimboukakis, Nikos ; Tambouratzis, George
Author_Institution :
Inst. of Language & Speech Process, Athens
fYear :
2007
fDate :
23-26 July 2007
Firstpage :
1
Lastpage :
6
Abstract :
The present article investigates the effectiveness of neural network models when applied to the task of categorising texts in the Greek language based on the style of their authors. Multilayer perceptrons (MLP), radial basis function networks (RBF) and self-organizing maps (SOM) are comparatively studied on the task of classifying documents based on a set of countable stylistic features. This task is of particular importance for information retrieval applications that involve very large databases of documents where the manual classification is extremely labour-intensive.
Keywords :
database management systems; information retrieval; multilayer perceptrons; natural language processing; neural nets; radial basis function networks; self-organising feature maps; Greek language; author attribution; documents databases; information retrieval applications; multilayer perceptrons; neural networks; radial basis function networks; self-organizing maps; text categorisation; Frequency; Information retrieval; Multilayer perceptrons; Natural languages; Neural networks; Radial basis function networks; Self organizing feature maps; Spatial databases; Speech processing; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007. IEEE International
Conference_Location :
London
ISSN :
1098-7584
Print_ISBN :
1-4244-1209-9
Electronic_ISBN :
1098-7584
Type :
conf
DOI :
10.1109/FUZZY.2007.4295356
Filename :
4295356
Link To Document :
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