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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 A Deep Neural Network-Based Model for Named Entity Recognition for Hindi Language Sharma, Richa; Agarwal, Basant; Khan, Mohammad S.... In: ETSU Faculty Works (2020) BASE Show details 2 Sentiment Analysis Using Common-Sense and Context Information Agarwal, Basant; Mittal, Namita; Bansal, Pooja. - : Hindawi Publishing Corporation, 2015 BASE Show details 3 Dependency-based semantic parsing for concept-level text analysis Agarwal, Basant; Poria, Soujanya; Gelbukh, Alexander; Howard, Newton; Hussain, Amir. - : Springer, 2014. : Berlin Heidelberg, 2014 Abstract: Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks. Concept extraction from text is a key step in concept-level text analysis. In this paper, we propose a ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses. Our approach is domain-independent and is able to extract concepts from heterogeneous text. Through this parsing technique, 92.21% accuracy was obtained on a dataset of 3,204 concepts. We also show experimental results on three different text analysis tasks, on which the proposed framework outperformed state-of-the-art parsing techniques. URL: https://doi.org/10.1007/978-3-642-54906-9_10 http://dspace.stir.ac.uk/bitstream/1893/20590/1/Dependency-based%20semantic%20parsing%202014.pdf http://hdl.handle.net/1893/20590 BASE Hide details

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