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Bio-Molecular Event Extraction with Markov Logic
| Citations: | 2 - 0 self |
BibTeX
@MISC{Riedel_bio-molecularevent,
author = {Sebastian Riedel and Rune Sætre and Hong-woo Chun and Toshihisa Takagi},
title = {Bio-Molecular Event Extraction with Markov Logic},
year = {}
}
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Abstract
This article presents a novel approach to event extraction from biological text using Markov Logic. It can be described by three design decisions: (1) instead of building a pipeline using local classifiers, we design and learn a joint probabilistic model over events in a sentence; (2) instead of developing specific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learning language, for this task; (3) we represent events as relations over the token indices of a sentence, as opposed to structures that relate event entities to gene or protein mentions. In this article we extend our original work (Riedel et al., 2009) by providing an error analysis for binding events. Moreover, we investigate the impact of different loss functions to precision, recall and F-measure. Finally, we show how to extract events of different types that share the same event clue. This extension allowed us to improve our performance our performance even further, leading to the third best scores for task 1 (in close range to the second place) and the best results for task 2 with a 14 percent point margin.
Keyphrases
markov logic bio-molecular event extraction learning algorithm original work specific inference different type event clue biological text token index event entity error analysis joint model local classifier event extraction novel approach joint probabilistic model protein mention percent point margin second place different loss function design decision
