Universiteit van Amsterdam

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Mostafa Dehghani wins Best Paper Award at ICTIR2016

ICTIR_Award.jpg

Mostafa Dehghani, from ILLC received the Best Paper Award of The ACM International Conference on the Theory of Information Retrieval (ICTIR2016). The prize is received for the paper entitled "On Horizontal and Vertical Separation in Hierarchical Text Classification" (co-authored by Hosein Azarbonyad, Jaap Kamp, and Maarten Marx).

Abstract of the paper:

Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of “separable” models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer,but also in different layers. Our main findings are the followings. First, we analysethe importance of separability on the data representation in the task of classification and based on that, we introduce a “Strong Separation Principle” for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Hierarchical Significant Words Language Models (HSWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real worlddata and demonstrate that how HSWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure.

Please take a look at these posts on "From Probability Ranking Principle to Strong Separation Principle", and "Two-dimensional separability in Hierarchical Text Classification" for more information on the paper. Also here is the presentation slides.

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