Publication Type:

Journal Article

Source:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, Volume 10478 LNCS, p.50-60 (2018)

ISBN:

9783319736051

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041842036&doi=10.1007%2f978-3-319-73606-8_4&partnerID=40&md5=15d478d0e3104ca095727494b29bf6b2

Keywords:

Classification (of information), Classification tasks, Concept-based, Data set, Information Retrieval, Research problems, Semantics, Text Analytics, Text classification, Text processing, Text representation, Vector space models, Vector spaces, Vectors

Abstract:

<p>This paper assesses the performance of frequency and concept based text representation in Mixed Script Information Retrieval and Classification tasks. In text analytics, representation serves as an unresolved research problem to progress further towards different applications. In this paper observations from different text representation methods in text classification and information retrieval are presented. The data set from the Mixed Script Information Retrieval shared task is used in this experiment and the performance of final submitted model is evaluated by task organizers. It is observed that distributional representation performs better than the frequency based text representation methods. The final system attained first place in task 2 and was 3.89% lesser than the top scored system in task 1. © Springer International Publishing AG. 2018.</p>

Notes:

cited By 0; Conference of International Workshop on Text Processing, FIRE 2016 ; Conference Date: 7 December 2016 Through 10 December 2016; Conference Code:210099

Cite this Research Publication

H. B. Barathi Ganesh, M. Kumar, A., and Soman, K. P., “From Vector Space Models to Vector Space Models of Semantics”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10478 LNCS, pp. 50-60, 2018.

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