Detecting Semantic Uncertainty by Learning Hedge Cues in Sentences Using an HMM
Xiujun Li, Wei Guang Gao, JUDE W. SHAVLIK · WORLD SCIENTIFIC eBooks · 2017
Detecting speculative assertions is essential to distinguish seman-tically uncertain information from the factual ones in text. This is critical to the trustworthiness of many intelligent systems that are based on information retrieval and natural language processing techniques, such as question answering or information extraction. We empirically explore three fundamental issues of uncertainty de-tection: (1) the predictive ability of different learning methods on this task; (2) whether using unlabeled data can lead to a more ac-curate model; and (3) whether closed-domain training or cross-domain training is better. For these purposes, we adopt two sta-tistical learning approaches to this problem: the commonly used bag-of-words model based on Naive Bayes, and the sequence la-beling approach using a Hidden Markov Model (HMM). We em-pirically compare between our two approaches as well as externally