Overview of BioNLP'09 shared task on event extraction

Kim, Jin-Dong; Ohta, Tomoko; Pyysalo, Sampo; Kano, Yoshinobu; Tsujii, Jun’ichi

doi:10.3115/1572340.1572342

Cited by 403 publications

(477 citation statements)

References 14 publications

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“…While event definitions have been proposed in several prior studies, existing approaches vary in how they model various linguistic forms such as nominal events, stative events, generic events, and light verbs (Pustejovsky et al, 2003;Palmer et al, 2005;Meyers et al, 2004;Kim et al, 2009;Song et al, 2015). Even with a formal and precise account of events, training annotators to learn all such linguistic intricacies remains a practical challenge.…”

Section: Related Workmentioning

confidence: 99%

Event Detection and Factuality Assessment with Non-Expert Supervision

Lee¹,

Artzi²,

Choi³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Events are communicated in natural language with varying degrees of certainty. For example, if you are "hoping for a raise," it may be somewhat less likely than if you are "expecting" one. To study these distinctions, we present scalable, highquality annotation schemes for event detection and fine-grained factuality assessment. We find that non-experts, with very little training, can reliably provide judgments about what events are mentioned and the extent to which the author thinks they actually happened. We also show how such data enables the development of regression models for fine-grained scalar factuality predictions that outperform strong baselines.

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Section: Related Workmentioning

confidence: 99%

Event Detection and Factuality Assessment with Non-Expert Supervision

Lee¹,

Artzi²,

Choi³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…We refer to an ordered triple rel = (p 1 , r, p 2 ), where r is an event or relation type denoting a directed relation (e.g., an expression, an inhibition) between proteins p 1 and p 2 as typed interaction. Note that this task is a subtask of event extraction (Kim et al, 2009) and an extension to protein-protein interaction detection (PPIs), where we want to predict if a protein pair (in any order) interacts in some way (Choi, 2016;Airola et al, 2008). Our whole pipeline is depicted in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

Towards Confidence Estimation for Typed Protein-Protein Relation Extraction

Thorne¹,

Klinger²

2017

Proceedings of the Biomedical NLP Workshop

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Systems which build on top of information extraction are typically challenged to extract knowledge that, while correct, is not yet well-known. We hypothesize that a good confidence measure for relational information has the property that such interesting information is found between information extracted with very high confidence and very low confidence. We discuss confidence estimation for the domain of biomedical protein-protein relation discovery in biomedical literature. As facts reported in papers take some time to be validated and recorded in biomedical databases, such task gives rise to large quantities of unknown but potentially true candidate relations. It is thus important to rank them based on supporting evidence rather than discard them. In this paper, we discuss this task and propose different approaches for confidence estimation and a pipeline to evaluate such methods. We show that the most straight-forward approach, a combination of different confidence measures from pipeline modules seems not to work well. We discuss this negative result and pinpoint potential future research directions.

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“…Similar to previous BioNLP shared tasks in 2009 and 2011 (Kim et al, 2009;Kim et al, 2011), this task focuses on molecular information extraction. The task organisers provided paragraphs from manually selected full text publications on seed development of Arabidopsis thaliana 1 Source: https://github.com/unimelbbionlp/BioNLPST2016/ annotated with mentions of biological entities like proteins and genes, and binary relations like Exists In Genotype and Occurs In Genotype.…”

Section: Introductionmentioning

confidence: 99%

SeeDev Binary Event Extraction using SVMs and a Rich Feature Set

Panyam¹,

Khirbat²,

Verspoor³

et al. 2016

Proceedings of the 4th BioNLP Shared Task Workshop

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This paper describes the system details and results of the participation of the team from the University of Melbourne in the SeeDev binary event extraction of BioNLP-Shared Task 2016. This task addresses the extraction of genetic and molecular mechanisms that regulate plant seed development from the natural language text of the published literature. In our submission, we developed a system 1 using a support vector machine classifier with linear kernel powered by a rich set of features. Our system achieved an F1-score of 36.4%.

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Overview of BioNLP'09 shared task on event extraction

Cited by 403 publications

References 14 publications

Event Detection and Factuality Assessment with Non-Expert Supervision

Event Detection and Factuality Assessment with Non-Expert Supervision

Towards Confidence Estimation for Typed Protein-Protein Relation Extraction

SeeDev Binary Event Extraction using SVMs and a Rich Feature Set

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