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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 – 4 of 4 1 Context-Aware Semantic Association Ranking Aleman-Meza, Boanerges; Halaschek, Chris; Arpinar, I. Budak... In: Amit P. Sheth (2016) BASE Show details 2 ezDI's Semantics-Enhanced Linguistic, NLP, and ML Approach for Health Informatics Goswami, Raxit; Shah, Neil; Sheth, Amit P. In: Amit P. Sheth (2016) BASE Show details 3 Intent Classification of Short-Text on Social Media Purohit, Hemant; Dong, Guozhu; Shalin, Valerie L.... In: Amit P. Sheth (2016) BASE Show details 4 Intent Classification of Short-Text on Social Media Purohit, Hemant; Dong, Guozhu; Shalin, Valerie L.; Thirunarayan, Krishnaprasad; Sheth, Amit P. In: Krishnaprasad Thirunarayan (2016) Abstract: Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis events. Our novel method exploits a hybrid feature representation created by combining top-down processing using knowledge-guided patterns with bottom-up processing using a bag-of-tokens model. We employ pattern-set creation from a variety of knowledge sources including psycholinguistics to tackle the ambiguity challenge, social behavior about conversations to enrich context, and contrast patterns to tackle the sparsity challenge. Our results show a significant absolute gain up to 7% in the F1 score relative to a baseline using bottom-up processing alone, within the popular multiclass frameworks of One-vs-One and One-vs-All. Intent mining can help design efficient cooperative information systems between citizens and organizations for serving organizational information needs. Keyword: Bioinformatics; Communication; Communication Technology and New Media; Computer Sciences; Databases and Information Systems; Life Sciences; OS and Networks; Physical Sciences and Mathematics; Science and Technology Studies; Social and Behavioral Sciences URL: https://works.bepress.com/tk_prasad/104 BASE Hide details

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