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Semantic Topic Compass – Classification Based on Unsupervised Feature Ambiguity Gradation

Cano, Amparo Elizabeth; Saif, Hassan; Alani, Harith and Motta, Enrico (2016). Semantic Topic Compass – Classification Based on Unsupervised Feature Ambiguity Gradation. Lecture Notes in Computer Science, 9678 pp. 350–367.

DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-34129-3_22
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Abstract

Characterising social media topics often requires new features to be continuously taken into account, and thus increasing the need for classifier retraining. One challenging aspect is the emergence of ambiguous features, which can affect classification performance. In this paper we investigate the impact of the use of ambiguous features in a topic classification task, and introduce the Semantic Topic Compass (STC) framework, which characterises ambiguity in a topics feature space. STC makes use of topic priors derived from structured knowledge sources to facilitate the semantic feature grading of a topic. Our findings demonstrate the proposed framework offers competitive boosts in performance across all datasets.

Item Type: Journal Item
Copyright Holders: 2016 Springer International Publishing
ISSN: 0302-9743
Keywords: topic classification; feature engineering; semantics
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Centre for Policing Research and Learning (CPRL)
Item ID: 48479
Depositing User: Kay Dave
Date Deposited: 13 Feb 2017 14:17
Last Modified: 01 Nov 2017 09:39
URI: http://oro.open.ac.uk/id/eprint/48479
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