Machine Learning for Text Document Relevan...

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Sprache: Englisch

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Beschreibung

The context oriented information retrieval has always been based on some or the other explicit ontologies. The emphasis is laid on on the Implicit Ontologies extracted from input text documents themselves. The research focuses upon design of a system (tool) to rank text documents available in machine-readable format by analyzing them upon softcopies of the syllabus content, through congenial content filtering techniques. The notion of n-gram co-occurrences is used to give the semantic interpretation to the core sentences and their neighboring components. The semantic depths of search key phrases can be learnt by analyzing term-to-term associations from the underlying conceptual dependencies of the extracted content. Two metric measures were chosen for exploring text-semantic depths namely, Topical boundaries and Topical vicinities. The degree of relative relevance was investigated by computing other relevance metric, contextual levels of term-significance from the filtered pages with meaningfully related content. The text-document ranking results were compared for both relevance number and fuzzy-ordering approaches and were found interpretable in finite directions.

Über den Autor

Dr. [...] is working as Associate Professor in Department of Information Technology at Bhilai Institute of Technology, Durg, Chhattisgarh, India. She has 11 journal and 19 conference publications to her credit. She was awarded Ph.d. in 2013. She is a life member of professional bodies: ISTE and CSI, India.

Erscheinungsjahr:	2014
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	180 S.
ISBN-13:	9783659233456
ISBN-10:	3659233455
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Rawal, Arpana Sharma, H. R. Kowar, Mahoj Kumar
Hersteller:	LAP LAMBERT Academic Publishing
Verantwortliche Person für die EU:	BoD - Books on Demand, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße:	220 x 150 x 12 mm
Von/Mit:	Arpana Rawal (u. a.)
Erscheinungsdatum:	27.03.2014
Gewicht:	0,286 kg

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