loading
Documents

Research.Publish.Connect.

Paper

Authors: Giacomo Frisoni ; Gianluca Moro and Antonella Carbonaro

Affiliation: Department of Computer Science and Engineering – DISI, University of Bologna, Via dell’Università 50, I-47522, Cesena, Italy

ISBN: 978-989-758-440-4

Keyword(s): Text Mining, Descriptive Analytics, Explainability, Latent Semantic Analysis, Unsupervised Learning, Rare Diseases.

Abstract: Though the strong evolution of knowledge learning models has characterized the last few years, the explanation of a phenomenon from text documents, called descriptive text mining, is still a difficult and poorly addressed problem. The need to work with unlabeled data, explainable approaches, unsupervised and domain independent solutions further increases the complexity of this task. Currently, existing techniques only partially solve the problem and have several limitations. In this paper, we propose a novel methodology of descriptive text mining, capable of offering accurate explanations in unsupervised settings and of quantifying the results based on their statistical significance. Considering the strong growth of patient communities on social platforms such as Facebook, we demonstrate the effectiveness of the contribution by taking the short social posts related to Esophageal Achalasia as a typical case study. Specifically, the methodology produces useful explanations about the exp eriences of patients and caregivers. Starting directly from the unlabeled patient’s posts, we derive correct scientific correlations among symptoms, drugs, treatments, foods and so on. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.8.102

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Frisoni, G.; Moro, G. and Carbonaro, A. (2020). Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining.In Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-440-4, pages 121-132. DOI: 10.5220/0009892001210132

@conference{data20,
author={Frisoni, G. and Gianluca Moro. and Antonella Carbonaro.},
title={Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2020},
pages={121-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009892001210132},
isbn={978-989-758-440-4},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining
SN - 978-989-758-440-4
AU - Frisoni, G.
AU - Moro, G.
AU - Carbonaro, A.
PY - 2020
SP - 121
EP - 132
DO - 10.5220/0009892001210132

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.