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Articles 1 - 3 of 3
Full-Text Articles in Library and Information Science
Discerning Emotions In Texts, Victoria L. Rubin, Jeffrey M. Stanton, Elizabeth D. Liddy
Discerning Emotions In Texts, Victoria L. Rubin, Jeffrey M. Stanton, Elizabeth D. Liddy
School of Information Studies - Faculty Scholarship
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. …
Context-Based Question-Answering Evaluation, Elizabeth D. Liddy, Anne R. Diekema, Ozgur Yilmazel
Context-Based Question-Answering Evaluation, Elizabeth D. Liddy, Anne R. Diekema, Ozgur Yilmazel
School of Information Studies - Faculty Scholarship
In this poster, we will present the results of efforts we have undertaken to conduct evaluations of a QA system in a real world environment and to understand the nature of the dimensions on which users evaluate QA systems when given full reign to comment on whatever dimensions they deem important.
Certainty Categorization Model, Elizabeth D. Liddy, Noriko Kando, Victoria L. Rubin
Certainty Categorization Model, Elizabeth D. Liddy, Noriko Kando, Victoria L. Rubin
School of Information Studies - Faculty Scholarship
We present a theoretical framework and preliminary results for manual categorization of explicit certainty information in 32 English newspaper articles. The explicit certainty markers were identified and categorized according to the four hypothesized dimensions – perspective, focus, timeline, and level of certainty. One hundred twenty one sentences from sample news stories contained a significantly lower frequency of markers per sentence (M=0.46, SD =0.04) than 564 sentences from sample editorials (M=0.6, SD =0.23), p= 0.0056, two-tailed heteroscedastic t-test. Within each dimension, editorials had most numerous markers per sentence in high level of certainty, writer’s point of view, and future and present …