Bermejo Ferrer, José Ángel

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Recognizing textual entailment
(2022-07-08) Bermejo Ferrer, José Ángel; Vega Riveros, José Fernando; College of Engineering; Alers Valentín, Hilton; Santiago Santiago, Nayda; Department of Electrical and Computer Engineering; Villanueva Vega, Marién
Understanding meaning and being able to perform inferences based on natural language text is an instinctive, subconscious process for human speakers, as we are able to quickly derive inferences from text, questions and received information. Entailment is a type of natural language inference (NLI) in which, by definition, a premise P entails a hypothesis H when H must be true if P is true. In this work, we examine the complexity of the semantic relation of entailment and develop a system that detects this relation via the use of thematic roles by means of the Universal Networking Language (UNL). The system was implemented in Python using ETAP-3 as its UNL- Enconverter and tested against two corpora to varying degrees of success. Our results show the significance of theta roles in detecting entailment. Our findings also coincide with observations made in recent work using neural based NLI systems: the need for these systems to learn the semantic effects of monotonicity reasoning, as well as the benefits of symbolic approaches to efficiently perform semantic processes of natural language inference.