Semantic Topic Compass – Classification Based on Unsupervised Feature Ambiguity Gradation

  • Amparo Elizabeth Cano
  • Hassan Saif
  • Harith Alani
  • Enrico Motta
Conference paper

DOI: 10.1007/978-3-319-34129-3_22

Volume 9678 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Cano A.E., Saif H., Alani H., Motta E. (2016) Semantic Topic Compass – Classification Based on Unsupervised Feature Ambiguity Gradation. In: Sack H., Blomqvist E., d'Aquin M., Ghidini C., Ponzetto S., Lange C. (eds) The Semantic Web. Latest Advances and New Domains. ESWC 2016. Lecture Notes in Computer Science, vol 9678. Springer, Cham

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.

Keywords

Topic classification Feature engineering Semantics 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Amparo Elizabeth Cano
    • 1
  • Hassan Saif
    • 1
  • Harith Alani
    • 1
  • Enrico Motta
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK