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• W2031100501 abstract "In context with the recent developments in the understanding of text semantics at machine level, this paper is an attempt to extract some of the most crucial fragments that play a key role as semantic units in natural language text. The context is intuitively extracted from typed dependency structures basically depicting dependency relations using the relevant Part-Of-Speech tagged representation of the text. These relations imply deep, fine grained, labeled dependencies that encode long-distance relations and passive information. The present work focuses on extracting the key noun phrases participating both in subject and object roles that are intended to be subsequently used in framing sentential components for model answers in any selected working domain." @default.
• W2031100501 created "2016-06-24" @default.
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• W2031100501 date "2010-10-01" @default.
• W2031100501 modified "2023-09-27" @default.
• W2031100501 title "Exploring Text Semantics to Extract Key-Fragments for Model Answers" @default.
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• W2031100501 doi "https://doi.org/10.1109/artcom.2010.110" @default.
• W2031100501 hasPublicationYear "2010" @default.
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