A Lucene and maximum entropy model based hedge detection system

Authors:
Lin Chen;Barbara Di Eugenio
Affiliations:
University of Illinois at Chicago, Chicago, IL;University of Illinois at Chicago, Chicago, IL
Venue:
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Year:
2010

Citing 2
Cited 1

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Abstract

This paper describes the approach to hedge detection we developed, in order to participate in the shared task at CoNLL-2010. A supervised learning approach is employed in our implementation. Hedge cue annotations in the training data are used as the seed to build a reliable hedge cue set. Maximum Entropy (MaxEnt) model is used as the learning technique to determine uncertainty. By making use of Apache Lucene, we are able to do fuzzy string match to extract hedge cues, and to incorporate part-of-speech (POS) tags in hedge cues. Not only can our system determine the certainty of the sentence, but is also able to find all the contained hedges. Our system was ranked third on the Wikipedia dataset. In later experiments with different parameters, we further improved our results, with a 0.612 F-score on the Wikipedia dataset, and a 0.802 F-score on the biological dataset.