FrameNet CNL: a Knowledge Representation and Information Extraction Language
This work addresses the challenge of integrating information extraction and controlled natural languages for news agencies, though it appears incremental as it builds on existing FrameNet and CNL concepts.
The paper tackles the problem of information extraction and knowledge representation from natural language documents by introducing FrameNet-CNL, a framework that extracts knowledge into a Frame-ontology and generates unambiguous paraphrases in multiple languages, with a state-of-the-art parser used by a national news agency.
The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.