Semantic relations across syntactic levels

Abstract

In order to make sense of a message conveyed to us via a spoken or written utterance, we understand what things are talked about, and how they are connected. From this point of view, do these sentences convey different messages? (1) I will arrive at 11 am. and I will arrive when you arrive. (2) I will meet you in the office. and I will meet you where we met last time. (3) Sweets before dinner spoil your appetite. and (4) Eating sweets before dinner spoils your appetite. I will arrive at a certain point in time: at 11 am., or when you arrive. I will meet you at a certain place: in the office, or where we met last time. We can talk about sweets and mean eating sweets. Literature review suggests that the relations exemplified by these pairs of sentences are different, because they connect different types of syntactic units. The first relation in each pair connects a verb and one of its arguments, the second---two clauses. Such distinctions are artificial. Semantic relations link concepts, and will surface on the syntactic level on which the concepts they connect surface. We aim to give an account of semantic relations that does not depend on syntactic levels. We will justify a unified view of semantic relations across syntactic levels. Such a view has a positive effect on text analysis. It will allow us to gather evidence for a particular semantic relation from all levels at which it appears. Having such information that is not separated according to syntactic levels will allow a text analysis and knowledge acquisition system to use at each processing step, all the evidence previously gathered. We will show that this translates into faster learning and better results. We can take semantic relation analysis onto another level. We can look for descriptions of concepts connected by a specific semantic relation to find what characteristics or features of the concepts connected make them interact in this way. (1) blue book, happy person, interesting study; (2) paper bag, wooden chair, iron gate; (3) oak tree, cumulus cloud, flounder fish. Blue, happy, interesting are properties, and paper, wood, iron are materials. Oak is a specific type of tree, cumulus is a type of cloud, and flounder is a type of fish. We will use ontologies to find similarities between concepts that explain or give us indications about the semantic relations in which they are involved. All these aspects we explore serve to improve text analysis. We propose a uniform processing of texts that allows us to extracts pairs of concepts that interact, and to describe this interaction through semantic relations.