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A neural network approach to selectional preference acquisition

Tim van de Cruys 1 
1 IRIT-MELODI - MEthodes et ingénierie des Langues, des Ontologies et du DIscours
IRIT - Institut de recherche en informatique de Toulouse
Abstract : This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applications, we propose a neural network modelthat learns to discriminate between felicitous and infelicitous arguments for a particular predicate. The model is entirely unsupervised preferences are learned fromunannotated corpus data. We propose twoneural network architectures: one that handles standard two-way selectional prefer-ences and one that is able to deal with multi-way selectional preferences. Themodel’s performance is evaluated on a pseudo-disambiguation task, on which it is shown to achieve state of the art performance.
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Tim van de Cruys. A neural network approach to selectional preference acquisition. Empirical Methods in Natural Language Processing (EMNLP), Oct 2014, Doha, Qatar. pp.26-35, ⟨10.3115/v1/D14-1004⟩. ⟨hal-02878928⟩



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