Publication Type:

Conference Paper

Source:

CEUR Workshop Proceedings, CEUR-WS, Volume 1737, p.201-204 (2016)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006093185&partnerID=40&md5=b883e3e5eae0dcb01677abbae1b66dcd

Keywords:

Classification (of information), Decision trees, Distributional semantics, Document matrices, Factorization, Health science, Information Retrieval, Information retrieval systems, Matrix algebra, Nonnegative matrix factorization, Search engines, Semantics, Text classification, Text processing

Abstract:

This paper describes about the our proposed system in the Consumer Health Information Search (CHIS) task. The objective of the task 1 is to classify the sentences in the document into relevant or irrelevant with respect to the query and task 2 is analysing the sentiment of the sentences in the documents with respect to the given query. In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem. In our experiment, Non - Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random Forest Tree utilized to perform the classification. The proposed approach yields 70.19% in task 1 and 34.64% in task 2 as an average accuracy.

Notes:

cited By 0; Conference of 2016 Forum for Information Retrieval Evaluation, FIRE 2016 ; Conference Date: 7 December 2016 Through 10 December 2016; Conference Code:125007

Cite this Research Publication

H. B. Barathi Ganesh, Dr. M. Anand Kumar, and Dr. Soman K. P., “Distributional semantic representation in health care text classification”, in CEUR Workshop Proceedings, 2016, vol. 1737, pp. 201-204.

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