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Spatial multi-arrangement for clustering and multi-way similarity dataset construction

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McCarthy, D 
van den Bosch, J 
Kriegeskorte, N 
Vulic, I 


We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems. Our work targets verb similarity, but the methodology is equally applicable to other parts of speech. Our approach circumvents the bottleneck of slow and expensive manual development of lexical resources by leveraging semantic intuitions of native speakers and adapting a spatial multi-arrangement approach from cognitive neuroscience, used before only with visual stimuli, to lexical stimuli. Our approach critically obtains judgments of word similarity in the context of a set of related words, rather than of word pairs in isolation. We also handle lexical ambiguity as a natural consequence of a two-phase process where verbs are placed in broad semantic classes prior to the fine-grained spatial similarity judgments. Our proposed design produces a large-scale verb resource comprising 17 relatedness-based classes and a verb similarity dataset containing similarity scores for 29,721 unique verb pairs and 825 target verbs, which we release with this paper.



Lexicon, Lexical Database, Semantics, Crowdsourcing

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LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

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LREC 2020 the 12th International Conference on Language Resources and Evaluation (LREC 2020)

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European Language Resources Association
European Research Council (648909)
ESRC (1804172)