Hedge Detection Using the RelHunter Approach
Eraldo Rezende Fernandes, Carlos Eduardo Meger Crestana, Ruy Luiz Milidiú · Conference on Computational Natural Language Learning · 2010
RelHunter is a Machine Learning based method for the extraction of structured information from text. Here, we apply RelHunter to the Hedge Detection task, proposed as the CoNLL-2010 Shared Task. RelHunter's key design idea is to model the target structures as a relation over entities. The method decomposes the original task into three subtasks: (i) Entity Identification; (ii) Candidate Relation Generation; and (iii) Relation Recognition. In the Hedge Detection task, we define three types of entities: cue chunk, start scope token and end scope token. Hence, the Entity Identification subtask is further decomposed into three token classification subtasks, one for each entity type. In the Candidate Relation Generation sub-task, we apply a simple procedure to generate a ternary candidate relation. Each instance in this relation represents a hedge candidate composed by a cue chunk, a start scope token and an end scope token. For the Relation Recognition subtask, we use a binary classifier to discriminate between true and false candidates. The four classifiers are trained with the Entropy Guided Transformation Learning algorithm. When compared to the other hedge detection systems of the CoNLL shared task, our scheme shows a competitive performance. The F-score of our system is 54.05 on the evaluation corpus.