An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language ques… Mehr…
An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. For example for the given question of "what is the capital of the Netherlands?", the task of question classification is to classify this question to the category "city" since the answer type of this question is of type "city". Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we introduce two new semantic features which improve the accuracy of classification. Furthermore, we developed a weighed approach to optimally combine different features. We also applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. Our experimental results show that our approach is successful. Buch / Mathematik, Naturwissenschaft & Technik / Informatik & EDV / Informatik<
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An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language ques… Mehr…
An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. For example for the given question of "what is the capital of the Netherlands?", the task of question classification is to classify this question to the category "city" since the answer type of this question is of type "city". Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we introduce two new semantic features which improve the accuracy of classification. Furthermore, we developed a weighed approach to optimally combine different features. We also applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. Our experimental results show that our approach is successful. Bücher / Naturwissenschaften, Medizin, Informatik & Technik / Informatik & EDV / Informatik<
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Babak Loni,Paperback, English-language edition,Pub by AV Akademikerverlag GmbH & Co. KG. Books Books ~~ Computers~~ Information Technology Enhanced-Question-Classification-with-Optimal-Co… Mehr…
Babak Loni,Paperback, English-language edition,Pub by AV Akademikerverlag GmbH & Co. KG. Books Books ~~ Computers~~ Information Technology Enhanced-Question-Classification-with-Optimal-Combination-of-Features~~Babak-Loni AV Akademikerverlag GmbH & Co. KG.<
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A new approach on automated question answering systems - Buch, gebundene Ausgabe, 88 S., Beilagen: Paperback, Erschienen: 2012 LAP Lambert Academic Publishing
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(*) Derzeit vergriffen bedeutet, dass dieser Titel momentan auf keiner der angeschlossenen Plattform verfügbar ist.
An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language ques… Mehr…
An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. For example for the given question of "what is the capital of the Netherlands?", the task of question classification is to classify this question to the category "city" since the answer type of this question is of type "city". Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we introduce two new semantic features which improve the accuracy of classification. Furthermore, we developed a weighed approach to optimally combine different features. We also applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. Our experimental results show that our approach is successful. Buch / Mathematik, Naturwissenschaft & Technik / Informatik & EDV / Informatik<
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An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language ques… Mehr…
An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. For example for the given question of "what is the capital of the Netherlands?", the task of question classification is to classify this question to the category "city" since the answer type of this question is of type "city". Question classification is typically done using machine learning techniques. Different lexical, syntactical and semantic features can be extracted from a question. In this work we introduce two new semantic features which improve the accuracy of classification. Furthermore, we developed a weighed approach to optimally combine different features. We also applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. Our experimental results show that our approach is successful. Bücher / Naturwissenschaften, Medizin, Informatik & Technik / Informatik & EDV / Informatik<
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Babak Loni,Paperback, English-language edition,Pub by AV Akademikerverlag GmbH & Co. KG. Books Books ~~ Computers~~ Information Technology Enhanced-Question-Classification-with-Optimal-Co… Mehr…
Babak Loni,Paperback, English-language edition,Pub by AV Akademikerverlag GmbH & Co. KG. Books Books ~~ Computers~~ Information Technology Enhanced-Question-Classification-with-Optimal-Combination-of-Features~~Babak-Loni AV Akademikerverlag GmbH & Co. KG.<
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A new approach on automated question answering systems - Buch, gebundene Ausgabe, 88 S., Beilagen: Paperback, Erschienen: 2012 LAP Lambert Academic Publishing
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Detailangaben zum Buch - Enhanced Question Classification with Optimal Combination of Features
EAN (ISBN-13): 9783847331346 ISBN (ISBN-10): 3847331345 Gebundene Ausgabe Taschenbuch Herausgeber: AV Akademikerverlag GmbH & Co. KG.
Buch in der Datenbank seit 2007-09-23T03:29:12+02:00 (Vienna) Detailseite zuletzt geändert am 2019-04-25T10:10:12+02:00 (Vienna) ISBN/EAN: 9783847331346
ISBN - alternative Schreibweisen: 3-8473-3134-5, 978-3-8473-3134-6
