With the amount of textual data increases rapidly, it is important to find not only appropriate, but also more trustworthy answers to user’s natural language questions. Natural language question answering systems have proven to be helpful to users because they can provide succinct answers that do not require users to wade through a large number of documents. In this workshop, we would like to discuss research outcomes of the Korean “Exobrain” project, consisting of natural language processing, information extraction, ontology reasoning and population, question answering technologies.
The theme of this workshop is to discuss recent progress of Natural language question answering technology for providing knowledge services with users.
The theme of this workshop is to discuss recent progress of Natural language question answering technology for providing knowledge services with users. Prospective authors are cordially invited to submit their original contributions covering completed or ongoing work related to the following research areas:
Natural language question analysis
IR-based question answering
Knowledge-based question answering
Natural language processing
Multi-agent communication and problem solving
Autonomous intelligent collaboration framework
Distributed task planning and execution
Ontology-based knowledgebase
Knowledge-based negotiation and verification
Intention recognition and analysis
Information extraction
Paraphrasing and textural entailments
Ontology population and reasoning
01月13日
2017
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