Paper: Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression

ACL ID W10-3003
Title Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2010
Authors

In this paper we describe our approach to the CoNLL-2010 shared task on de- tecting 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-enf...