SQuARE: Semantics-based Question Answering and Reasoning Engine

Question Answering Based on Distributional Semantics

An NLP application for question answering provides an insight into computer’s understanding of human language. Many areas of NLP have recently built on deep learning and distributional semantic representation. This paper seeks to apply distributional semantic models and convolutional neural networks to the question answering task.

Question Answering Using Ontological Semantics

This paper describes the initial results of an experiment in integrating knowledge-based text processing with real-world reasoning in a question answering system. Our MOQA “meaning-oriented question answering” system seeks answers to questions not in open text but rather in a structured fact repository whose elements are instances of ontological concepts extracted from the text meaning represen...

Deduction Engine Design for PNL-Based Question Answering System

In this paper, we present a methodology for designing a Precisiated Natural Language (PNL) based deduction engine for automated Question Answering (QA) systems. QA is one type of information retrieval system, and is regarded as the next advancement beyond keyword-based search engines, as it requires deductive reasoning and use of domain/background knowledge. PNL, as discussed by Zadeh, is one r...

Multiple-Engine Question Answering in TextMap

Knowledge and Reasoning for Medical Question-Answering

Restricted domains such as medicine set a context where question-answering is more likely expected to be associated with knowledge and reasoning (Mollá and Vicedo, 2007; Ferret and Zweigenbaum, 2007). On the one hand, knowledge and reasoning may be more necessary than in open-domain question-answering because of more specific or more difficult questions. On the other hand, it may also be more m...