Paper ID | HLT-13.6 | ||
Paper Title | ''YOU SHOULD PROBABLY READ THIS'': HEDGE DETECTION IN TEXT | ||
Authors | Denys Katerenchuk, The Graduate Center, CUNY, United States; Rivka Levitan, Brooklyn College, CUNY, United States | ||
Session | HLT-13: Information Extraction | ||
Location | Gather.Town | ||
Session Time: | Thursday, 10 June, 14:00 - 14:45 | ||
Presentation Time: | Thursday, 10 June, 14:00 - 14:45 | ||
Presentation | Poster | ||
Topic | Human Language Technology: [HLT-MLMD] Machine Learning Methods for Language | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus. |