Synopsis
Information Retrieval(IR) systems are gaining importance due to wide range of applications like recommender systems, search engines, etc., however, most of the IR systems use statistical methods built on top of bag-of-words approach for text retrieval. Graph-of-words approach is an alternative to bag-of-words approach that uses graph theoretic methods to rank keywords and related documents. We represent text documents as graphs whose vertices correspond to the unique terms belonging to the document. The edges represent co-occurrences between the terms. The underlying assumption is that the terms that co-occur have some sort of semantic relationship that can be harnessed for IR systems. The significant terms can be extracted using graph centrality measures. In this book, we have proposed a novel graph-of-words indexing technique using eigenvector scores that uses case separation for Gujarati language. We compared the performance of IR systems of our approach over the classical bag-of-words approach, mean average precision (MAP) values obtained in our experiments show that our approach has shown significant improvement over classical approaches.
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Identification of Significant Keywords from Gujarati Text Documents
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LAP LAMBERT Academic Publishing Jun 2019, 2019
ISBN 10: 6200082766
ISBN 13: 9786200082763
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Information Retrieval(IR) systems are gaining importance due to wide range of applications like recommender systems, search engines, etc., however, most of the IR systems use statistical methods built on top of bag-of-words approach for text retrieval. Graph-of-words approach is an alternative to bag-of-words approach that uses graph theoretic methods to rank keywords and related documents. We represent text documents as graphs whose vertices correspond to the unique terms belonging to the document. The edges represent co-occurrences between the terms. The underlying assumption is that the terms that co-occur have some sort of semantic relationship that can be harnessed for IR systems. The significant terms can be extracted using graph centrality measures. In this book, we have proposed a novel graph-of-words indexing technique using eigenvector scores that uses case separation for Gujarati language. We compared the performance of IR systems of our approach over the classical bag-of-words approach, mean average precision (MAP) values obtained in our experiments show that our approach has shown significant improvement over classical approaches. 164 pp. Englisch. Seller Inventory # 9786200082763
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Identification of Significant Keywords from Gujarati Text Documents
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ISBN 10: 6200082766
ISBN 13: 9786200082763
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Joshi Dr. HardikDr. Hardik Joshi is an Assistant Professor with the Dept. of Computer Sc., Gujarat University, India. His research interests include Natural Language Processing and Information Retrieval.Information Retrieval(IR) . Seller Inventory # 298918778
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Identification of Significant Keywords from Gujarati Text Documents
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200082766
ISBN 13: 9786200082763
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Taschenbuch. Condition: Neu. Identification of Significant Keywords from Gujarati Text Documents | Hardik Joshi | Taschenbuch | 164 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200082763 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 116849106
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Identification of Significant Keywords from Gujarati Text Documents
Published by
LAP LAMBERT Academic Publishing Jun 2019, 2019
ISBN 10: 6200082766
ISBN 13: 9786200082763
New
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Information Retrieval(IR) systems are gaining importance due to wide range of applications like recommender systems, search engines, etc., however, most of the IR systems use statistical methods built on top of bag-of-words approach for text retrieval. Graph-of-words approach is an alternative to bag-of-words approach that uses graph theoretic methods to rank keywords and related documents. We represent text documents as graphs whose vertices correspond to the unique terms belonging to the document. The edges represent co-occurrences between the terms. The underlying assumption is that the terms that co-occur have some sort of semantic relationship that can be harnessed for IR systems. The significant terms can be extracted using graph centrality measures. In this book, we have proposed a novel graph-of-words indexing technique using eigenvector scores that uses case separation for Gujarati language. We compared the performance of IR systems of our approach over the classical bag-of-words approach, mean average precision (MAP) values obtained in our experiments show that our approach has shown significant improvement over classical approaches.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Englisch. Seller Inventory # 9786200082763
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Identification of Significant Keywords from Gujarati Text Documents
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200082766
ISBN 13: 9786200082763
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Information Retrieval(IR) systems are gaining importance due to wide range of applications like recommender systems, search engines, etc., however, most of the IR systems use statistical methods built on top of bag-of-words approach for text retrieval. Graph-of-words approach is an alternative to bag-of-words approach that uses graph theoretic methods to rank keywords and related documents. We represent text documents as graphs whose vertices correspond to the unique terms belonging to the document. The edges represent co-occurrences between the terms. The underlying assumption is that the terms that co-occur have some sort of semantic relationship that can be harnessed for IR systems. The significant terms can be extracted using graph centrality measures. In this book, we have proposed a novel graph-of-words indexing technique using eigenvector scores that uses case separation for Gujarati language. We compared the performance of IR systems of our approach over the classical bag-of-words approach, mean average precision (MAP) values obtained in our experiments show that our approach has shown significant improvement over classical approaches. Seller Inventory # 9786200082763