A Method of Topic Detection for Great Volume of Data

Flora Amato, Francesco Gargiulo, Antonino Mazzeo, Carlo Sansone

2014

Abstract

Topics extraction has become increasingly important due to its effectiveness in many tasks, including information filtering, information retrieval and organization of document collections in digital libraries. The Topic Detection consists to find the most significant topics within a document corpus. In this paper we explore the adoption of a methodology of feature reduction to underline the most significant topics within a document corpus. We used an approach based on a clustering algorithm (X-means) over the t f −id f matrix calculated starting from the corpus, by which we describe the frequency of terms, represented by the columns, that occur in each document, represented by a row. To extract the topics, we build n binary problems, where n is the numbers of clusters produced by an unsupervised clustering approach and we operate a supervised feature selection over them considering the top features as the topic descriptors. We will show the results obtained on two different corpora. Both collections are expressed in Italian: the first collection consists of documents of the University of Naples Federico II, the second one consists in a collection of medical records.

Paper Citation

in Harvard Style

Amato F., Gargiulo F., Mazzeo A. and Sansone C. (2014). A Method of Topic Detection for Great Volume of Data . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014) ISBN 978-989-758-035-2, pages 434-439. DOI: 10.5220/0005145504340439

in Bibtex Style

@conference{komis14,
author={Flora Amato and Francesco Gargiulo and Antonino Mazzeo and Carlo Sansone},
title={A Method of Topic Detection for Great Volume of Data},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014)},
year={2014},
pages={434-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005145504340439},
isbn={978-989-758-035-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014)
TI - A Method of Topic Detection for Great Volume of Data
SN - 978-989-758-035-2
AU - Amato F.
AU - Gargiulo F.
AU - Mazzeo A.
AU - Sansone C.
PY - 2014
SP - 434
EP - 439
DO - 10.5220/0005145504340439