Applied morphological processing of English
Natural Language Engineering
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Overview of BioNLP'09 shared task on event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
The CoNLL-2010 shared task: learning to detect hedges and their scope in natural language text
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Speculation and negation: Rules, rankers, and the role of syntax
Computational Linguistics
| Hi-index | 0.00 |
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.