Semantic Parsing based on Verbal Subcategorization
Jordi Atserias, Irene Castellon, Montse Civit, German Rigau
TL;DR
This paper introduces a novel semantic parsing approach for unrestricted texts using a verbal subcategorization lexicon and pattern recognition, aiming to improve information extraction.
Contribution
It presents a new methodology combining verbal subcategorization with pattern recognition for semantic parsing of unrestricted texts.
Findings
Demonstrates effectiveness of LEXPIR in semantic parsing
Integrates verbal subcategorization with pattern recognition techniques
Enhances information extraction from complex texts
Abstract
The aim of this work is to explore new methodologies on Semantic Parsing for unrestricted texts. Our approach follows the current trends in Information Extraction (IE) and is based on the application of a verbal subcategorization lexicon (LEXPIR) by means of complex pattern recognition techniques. LEXPIR is framed on the theoretical model of the verbal subcategorization developed in the Pirapides project.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Speech and dialogue systems