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Certainty Identification in Texts: Categorization Model and Manual Tagging Results
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In: Victoria Rubin (2015)
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Questions to be Asked & Answered on NLP’s Role in Improving Semantic Annotation for IR
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In: School of Information Studies - Faculty Scholarship (2010)
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Certainty Identification in Texts: Categorization Model and Manual Tagging Results
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In: School of Information Studies - Faculty Scholarship (2006)
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Analyst-Focused Arabic Information Retrieval
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In: School of Information Studies - Faculty Scholarship (2005)
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7 |
Document Retrieval, Automatic
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In: School of Information Studies - Faculty Scholarship (2005)
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8 |
Hands-On NLP for an Interdisciplinary Audience
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In: School of Information Studies - Faculty Scholarship (2005)
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Improved Document Representation for Classification Tasks for the Intelligence Community
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In: School of Information Studies - Faculty Scholarship (2005)
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10 |
Advanced Capabilities for Evidence Extraction (ACEE)
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In: DTIC AND NTIS (2004)
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Semantic Analysis for Monitoring Insider Threats
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In: School of Information Studies - Faculty Scholarship (2004)
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Evaluation of Restricted Domain Question-Answering Systems
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In: School of Information Studies - Faculty Scholarship (2004)
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13 |
Discerning Emotions in Texts
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In: School of Information Studies - Faculty Scholarship (2004)
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Abstract:
We present an empirically verified model of discernable emotions, Watson and Tellegen’s Circumplex Theory of Affect from social and personality psychology, and suggest its usefulness in NLP as a potential model for an automation of an eight-fold categorization of emotions in written English texts. We developed a data collection tool based on the model, collected 287 responses from 110 non-expert informants based on 50 emotional excerpts (min=12, max=348, average=86 words), and analyzed the inter-coder agreement per category and per strength of ratings per sub-category. The respondents achieved an average 70.7% agreement in the most commonly identified emotion categories per text. The categories of high positive affect and pleasantness were most common in our data. Within those categories, the affective terms “enthusiastic”, “active”, “excited”, “pleased”, and “satisfied” had the most consistent ratings of strength of presence in the texts. The textual clues the respondents chose had comparable length and similar key words. Watson and Tellegen’s model appears to be usable as a guide for development of an NLP algorithm for automated identification of emotion in English texts, and the non-expert informants (with college degree and higher) provided sufficient information for future creation of a gold standard of clues per category.
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Keyword:
automation; Circumplex Theory of Affect; emotions; Library and Information Science; Linguistics; natural language processing; NLP; social psychology
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URL: https://surface.syr.edu/cgi/viewcontent.cgi?article=1041&context=istpub https://surface.syr.edu/istpub/65
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14 |
Context-Based Question-Answering Evaluation
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In: School of Information Studies - Faculty Scholarship (2004)
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Certainty Categorization Model
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In: School of Information Studies - Faculty Scholarship (2004)
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What do You Mean? Finding Answers to Complex Questions
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In: School of Information Studies - Faculty Scholarship (2003)
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17 |
A Breadth of NLP Applications
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In: School of Information Studies - Faculty Scholarship (2002)
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Transformation Based Learning for Specialization of Generic Event Extractions
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In: School of Information Studies - Faculty Scholarship (2002)
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An NLP Approach for Improving Access to Statistical Information for the Masses
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In: School of Information Studies - Faculty Scholarship (2001)
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20 |
Information Security and Sharing
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In: School of Information Studies - Faculty Scholarship (2001)
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