In this paper we describe our approach to the CoNLL-2010 shared task on detecting speculative language in biomedical text. We treat the detection of sentences containing uncertain information (Task1) as a token classification task since the existence or absence of cues determines the sentence label. We distinguish words that have speculative and non-speculative meaning by employing syntactic features as a proxy for their semantic content. In order to identify the scope of each cue (Task2), we learn a classifier that predicts whether each token of a sentence belongs to the scope of a given cue. The features in the classifier are based on the syntactic dependency path between the cue and the token. In both tasks, we use a Bayesian logistic regression classifier incorporating a sparsity-enforcing Laplace prior. Overall, the performance achieved is 85.21% F-score and 44.11% F-score in Task1 and Task2, respectively.
REF also developed a classifier to detect the information certainty in biomedical text, using syntactic dependencies and logistic regression.
Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression
Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Computer Science