Linguistic Approach to Semantic Correlation Rules

Abstract

As communication between humans and machines in natural language still seems essential, especially for end users, Natural Language Processing (NLP) methods are used to classify and interpret this. NLP, as a technology, combines grammatical, semantical, and pragmatical analyses with statistics or machine learning to make language logically understandable by machines and to allow new interpretations of data in contrast to predefined logical structures. Some NLP methods do not go far beyond a retrieving of the indexation of content. Therefore, indexation is considered as a very simple linguistic approach. Semantic correlation rules offer the possibility to retrieve easy semantic relations without a special tool by using a set of predefined rules. Therefore, this paper aims to examine, to which extend Semantic Correlation Rules (SCRs) will be able to retrieve linguistic semantic relations and to what extend a simple NLP method can be set up to allow further interpretation of data. In order to do so, an easy linguistic model was modelled by an indexation that is enriched with semantical relations to give data more context. These semantic relations were then queried by SCRs to set up an NLP method.