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- W2294941583 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 close r 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." @default.
- W2294941583 created "2016-06-24" @default.
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- W2294941583 date "2016-02-18" @default.
- W2294941583 modified "2023-09-30" @default.
- W2294941583 title "IR based Task-Model Learning: Automating the hierarchical structuring of tasks" @default.
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- W2294941583 doi "https://doi.org/10.3233/web-160330" @default.
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