Abstract
Inspired on psycholinguistics and neuroscience, a symbolic-connectionist hybrid system called θ-Pred (Thematic Pred ictor for natural language) is proposed, designed to reveal the thematic grid assigned to a sentence. Through a symbolic module, which includes anaphor resolution and relative clause processing, a parsing of the input sentence is performed, generating logical formulae based on events and thematic roles for Portuguese language sentences. Previously, a morphological analysis is carried out. The parsing displays, for grammatical sentences, the existing readings and their thematic grids. In order to disambiguate among possible interpretations, there is a connectionist module, comprising, as input, a featural representation of the words (based on verb/noun WordNet classification and on classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. θ-Pred employs biologically inspired training algorithm and architecture, adopting a psycholinguistic view of thematic theory.
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Rosa, J.L.G. (2007). A Connectionist Thematic Grid Predictor for Pre-parsed Natural Language Sentences. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_99
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DOI: https://doi.org/10.1007/978-3-540-72393-6_99
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