Knowledge-driven Natural Language Understanding of English Text and its Applications
Keywords:Question Answering, Common-Sense Reasoning
AbstractUnderstanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by ``truly understanding'' the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy.
How to Cite
Basu, K., Varanasi, S. C., Shakerin, F., Arias, J., & Gupta, G. (2021). Knowledge-driven Natural Language Understanding of English Text and its Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12554-12563. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17488
AAAI Technical Track on Speech and Natural Language Processing I