A study of metrics of distance and correlation between ranked lists for compositionality detection |
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Affiliation: | 1. Bernstein Center for Computational Neuroscience Berlin, Berlin Center for Advanced Neuroimaging, Cluster of Excellence NeuroCure, Department of Neurology, Charité Universitätsmedizin Berlin, Germany;2. Department of Psychology and Neuroimaging Center, Collaborative Research Center 940 “Volition and Cognitive Control”, Technische Universität Dresden, Germany;3. Berlin School of Mind and Brain, Department of Psychology, Humboldt-Universität zu Berlin, Germany |
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Abstract: | Compositionality in language refers to how much the meaning of some phrase can be decomposed into the meaning of its constituents and the way these constituents are combined. Based on the premise that substitution by synonyms is meaning-preserving, compositionality can be approximated as the semantic similarity between a phrase and a version of that phrase where words have been replaced by their synonyms. Different ways of representing such phrases exist (e.g., vectors (Kiela and Clark, 2013) or language models (Lioma, Simonsen, Larsen, and Hansen, 2015)), and the choice of representation affects the measurement of semantic similarity.We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics. In contrast to most state-of-the-art approaches in compositionality detection, our method is completely unsupervised. Experiments with a publicly available dataset of 1048 human-annotated phrases shows that, compared to strong supervised baselines, our approach provides superior measurement of compositionality using any of the distance and correlation metrics considered. |
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Keywords: | Compositionality detection Metrics of distance and correlation |
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