IR based Task-Model Learning: Automating the hierarchical structuring of tasks

Yusuke Fukazawa*, Mark Kröll, Markus Strohmaier, Jun Ota

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms.
Original languageEnglish
Pages (from-to)31-41
JournalWeb Intelligence and Agent Systems
Volume14
Issue number1
DOIs
Publication statusPublished - 2016

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Experimental

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