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Communicatie binnen het Zorgmentalitymodel van Motivaction. Presentatie tijdens zorgevenement van Bindinc. Custom Media op 1 december 2015 in Amersfoort
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Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specifi c domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first m odule was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last m odule performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and ident ifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation
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Communicatie binnen het Zorgmentalitymodel van Motivaction. Presentatie tijdens zorgevenement van Bindinc. Custom Media op 1 december 2015 in Amersfoort
Communicatie richting zorgconsumenten
Communicatie richting zorgconsumenten
Bindinc.
Passé composé
M2 Anass
M2 Anass
edmondvincent
Vestuario
Vestuario
Monstrica
Wanita
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Eka Widia
Question answering (QA) system aims at retrieving precise information from a large collection of documents against a query. This paper describes the architecture of a Natural Language Question Answering (NLQA) system for a specifi c domain based on the ontological information, a step towards semantic web question answering. The proposed architecture defines four basic modules suitable for enhancing current QA capabilities with the ability of processing complex questions. The first m odule was the question processing, which analyses and classifies the question and also reformulates the user query. The second module allows the process of retrieving the relevant documents. The next module processes the retrieved documents, and the last m odule performs the extraction and generation of a response. Natural language processing techniques are used for processing the question and documents and also for answer extraction. Ontology and domain knowledge are used for reformulating queries and ident ifying the relations. The aim of the system is to generate short and specific answer to the question that is asked in the natural language in a specific domain. We have achieved 94 % accuracy of natural language question answering in our implementation
Architecture of an ontology based domain-specific natural language question a...
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