Extracting Semantic Role Information from Unstructured Texts

Abstract

Shallow semantic parsing of natural language processing is an important component in all kind of NLP applications and Semantic Role Labeling in particular, is an active research topic. This paper describes a rule-based Semantic Role Labeling system aimed at extracting semantic information from texts. The input text is processed by exploiting part of speech information and syntactic dependencies in order to identify semantic roles. The system's architecture is presented and the results and further developments are discussed.