Open Access. Powered by Scholars. Published by Universities.®

Computational Linguistics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Semantics and Pragmatics

Selected Works

Articles 1 - 2 of 2

Full-Text Articles in Computational Linguistics

Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner Dec 2016

Acoustic Classification Of Focus: On The Web And In The Lab, Jonathan Howell, Mats Rooth, Michael Wagner

Jonathan Howell

We present a new methodological approach which combines both naturally-occurring speech harvested on the web and speech data elicited in the laboratory. This proof-of-concept study examines the phenomenon of focus sensitivity in English, in which the interpretation of particular grammatical constructions (e.g., the comparative) is sensitive to the location of prosodic prominence. Machine learning algorithms (support vector machines and linear discriminant analysis) and human perception experiments are used to cross-validate the web-harvested and lab-elicited speech. Results con rm the theoretical predictions for location of prominence in comparative clauses and the advantages using both web-harvested and lab-elicited speech. The most robust …

Go to article

Towards News Verification: Deception Detection Methods For News Discourse, Victoria Rubin, Niall Conroy, Yimin Chen Jan 2015

Towards News Verification: Deception Detection Methods For News Discourse, Victoria Rubin, Niall Conroy, Yimin Chen

Victoria Rubin

News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds. Truthful (authentic) news matches the writer’s knowledge. How do you tell the difference between the two in an automated way? To investigate this question, we analyzed rhetorical structures, discourse constituent parts and their coherence relations in deceptive and truthful news sample from NPR’s “Bluff the Listener”. Subsequently, we applied a vector space model to cluster the news by discourse feature similarity, achieving 63% accuracy. Our predictive model is not significantly better than chance …

Go to article