Learning and Inference for Clause Identification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Introduction to the CoNLL-2001 shared task: clause identification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A Token Classification Approach to Dependency Parsing
STIL '09 Proceedings of the 2009 Seventh Brazilian Symposium in Information and Human Language Technology
Clause Identification Using Entropy Guided Transformation Learning
STIL '09 Proceedings of the 2009 Seventh Brazilian Symposium in Information and Human Language Technology
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
A machine learning approach to Portuguese clause identification
PROPOR'10 Proceedings of the 9th international conference on Computational Processing of the Portuguese Language
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
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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.