Embedded controlled language to facilitate information extraction from eGov policies
The goal of this paper is to propose a system that can extract formal semantic knowledge representation from natural language eGov policies. We present an architecture that allows for extracting Controlled Natural Language (CNL) statements from heterogeneous natural language texts with the ability to support multilinguality. The approach is based on the concept of embedded CNLs.
Extracting Semantic Knowledge from Unstructured Text Using Embedded Controlled Language
Nowadays, most of the data on the Web is still in the form of unstructured text. Knowledge extraction from unstructured text is highly desirable but extremely challenging due to the inherent ambiguity of natural language. In this article, we present an architecture of an information extraction system based on the concept of Embedded Controlled Language that allows for extracting formal semantic knowledge from an unstructured text corpus. Moreover, the presented approach has a potential to support multilingual input and output.