The TEXTCAP semantic interpreter

Abstract

The lack of large amounts of readily available, explicitly represented knowledge has long been recognized as a barrier to applications requiring semantic knowledge such as machine translation and question answering. This problem is analogous to that facing machine translation decades ago, where one proposed solution was to use human translators to post-edit automatically produced, low quality translations rather than expect a computer to independently create high-quality translations. This paper describes an attempt at implementing a semantic parser that takes unrestricted English text, uses publically available computational linguistics tools and lexical resources and as output produces semantic triples which can be used in a variety of tasks such as generating knowledge bases, providing raw material for question answering systems, or creating RDF structures. We describe the TEXTCAP system, detail the semantic triple representation it produces, illustrate step by step how TEXTCAP processes a short text, and use its results on unseen texts to discuss the amount of post-editing that might be realistically required.