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공공누리This item is licensed Korea Open Government License

dc.contributor.author
SebastianRiedel
dc.contributor.author
전홍우
dc.date.accessioned
2019-08-28T07:41:03Z
dc.date.available
2019-08-28T07:41:03Z
dc.date.issued
2011-11-27
dc.identifier.issn
1467-8640
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/13971
dc.description.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 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% point margin.
dc.language
eng
dc.relation.ispartofseries
Computational Intelligence
dc.title
Bio-molecular event extraction with Markov Logic
dc.citation.endPage
582
dc.citation.number
4
dc.citation.startPage
558
dc.citation.volume
27
dc.subject.keyword
Text mining
dc.subject.keyword
Natural Language Processing
dc.subject.keyword
Relation Extraction
dc.subject.keyword
Machine Learning
Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
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