A Tool Design of Subjective Question Answering Using Text Mining - Couverture souple

Thomas, Ani; Sharma, H. R.; Sharma, Sanjay

 
9783659519970: A Tool Design of Subjective Question Answering Using Text Mining
Couverture souple
ISBN 10 : 3659519979 ISBN 13 : 9783659519970
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

The subjective answer evaluations performed on large scale, say in academia, show that human evaluations remain biased and are subject to specific guidelines given to human assessors. An attempt is made to generate unbiased system. The overall system’s functionality relies on three main components, namely, question processing component, the search component and the answer-evaluation component. The parts of e-book in the knowledge sources are annotated and classified according to the bootstrapping ontologies of the subject domain. In continuum with the efforts put by computational linguistic communities towards automatic retrieval of text semantics, an algorithm is designed to automatically generate ‘model answer’ for any keyed-in question or series of related questionnaires. An attempt is made to extract the most meaningful textual fragments from natural language sentences, highlighting the semantic sense of the explained discourse. The research focus roots upon extracting the noun phrases existing in dominating subject and object roles that are intended to connect to the verb phrases, forming triplet components for whole sentence or a part of it.

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Présentation de l'éditeur

The subjective answer evaluations performed on large scale, say in academia, show that human evaluations remain biased and are subject to specific guidelines given to human assessors. An attempt is made to generate unbiased system. The overall system’s functionality relies on three main components, namely, question processing component, the search component and the answer-evaluation component. The parts of e-book in the knowledge sources are annotated and classified according to the bootstrapping ontologies of the subject domain. In continuum with the efforts put by computational linguistic communities towards automatic retrieval of text semantics, an algorithm is designed to automatically generate ‘model answer’ for any keyed-in question or series of related questionnaires. An attempt is made to extract the most meaningful textual fragments from natural language sentences, highlighting the semantic sense of the explained discourse. The research focus roots upon extracting the noun phrases existing in dominating subject and object roles that are intended to connect to the verb phrases, forming triplet components for whole sentence or a part of it.

Biographie de l'auteur

Dr. (Mrs.) Ani Thomas is Professor and Head of the Department of Computer Applications, Bhilai Institute of Technology, Durg (C.G.) , India. She has published 9 international and 2 national journal papers and more than 20 conference papers. She is life member of ISTE, IDES and CSI. Her focus of research includes Text Mining and Machine Learning.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3659519979
ISBN 13 
9783659519970
Reliure
Broché
Nombre de pages
200
Coordonnées du fabricant
non disponible
Personne responsable
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