SECRET: Semantically Enhanced Classification of Real-World Tasks

Ayten Ozge Akmandor, Jorge Ortiz, Irene Manotas, Bongjun Ko, Niraj K. Jha

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among labels in the decision process. On the other hand, natural language processing (NLP) algorithms emphasize the importance of semantic information. In this article, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). SECRET performs classifications by fusing the semantic information of the labels with the available data: it combines the feature space of the supervised algorithms with the semantic space of the NLP algorithms and predicts labels based on this joint space. Experimental results indicate that, compared to traditional supervised learning, SECRET achieves up to 14.0 percent accuracy and 13.1 percent F1 score improvements. Moreover, compared to ensemble methods, SECRET achieves up to 12.7 percent accuracy and 13.3 percent F1 score improvements. This points to a new research direction for supervised classification based on incorporation of semantic information.

Original languageEnglish (US)
Article number9076254
Pages (from-to)440-456
Number of pages17
JournalIEEE Transactions on Computers
Issue number3
StatePublished - Mar 1 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


  • Feature space
  • inference
  • machine learning
  • natural language processing
  • semantic information
  • semantic space
  • word embedding


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