Semantic Answer Validation using Universal Networking Language

we present a rule-based answer validation (AV) system based on textual entailment (TE) recognition mechanism that uses semantic features expressed in the Universal Networking Language (UNL). We consider the question as the TE hypothesis (H) and the supporting text as TE text (T). Our proposed TE system compares the UNL relations in both T and H in order to identify the entailment relation as either validated or rejected. For training and evaluation, we used the AVE 2008 development set. We obtained 58% precision and 22% F-score for the decision “validated.” Keywords— question answering, textual entailment, Universal Networking Language, AVE 2008 data sets.