Abstract
A recent trend in the exploitation of unstructured text content is the use of natural language question answering (NLQA) systems. NLQA is an elaboration of traditional information retrieval techniques for satisfying a user's information needs, where the goal is not simply to retrieve relevant documents but to additionally extract specific passages and semantic entities from these documents as candidate answers to a natural language question. NLQA is thus a tight integration of natural language processing (NLP), information retrieval (IR) and information extraction (IE) designed to circumvent the deep and brittle analysis of questions in favor of shallow but robust comprehension, to ultimately achieve a broad domain question-answering competence. It is argued here that the key to achieving good quality answers in a high-throughput setting lies in a system's ability to construct rich queries that incorporate knowledge from multiple sources.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
