ArticleComputational intelligence2011
HIGH-PRECISION BIOLOGICAL EVENT EXTRACTION: EFFECTS OF SYSTEM AND OF DATA.
Article in Computational intelligence, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Developing a Physical Activity Ontology to Support the Interoperability of Physical Activity Data.Journal of medical Internet research · 2019Article
- Improving precision in concept normalization.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Multiple kernels learning-based biological entity relationship extraction method.Journal of biomedical semantics · 2017Article
- Leveraging syntax to better capture the semantics of elliptical coordinated compound noun phrases.Journal of biomedical informatics · 2017Article
- Knowledge-based biomedical Data Science.EPJ data science · 2017Article
- A corpus for plant-chemical relationships in the biomedical domain.BMC bioinformatics · 2016Article
- A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.BMC bioinformatics · 2014Article
- Studying PubMed usages in the field for complex problem solving: Implications for tool design.Journal of the American Society for Information Science and Technology : JASIST · 2013Article
- Annotating the biomedical literature for the human variome.Database : the journal of biological databases and curation · 2013Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
We approached the problems of event detection, argument identification, and negation and speculation detection in the BioNLP'09 information extraction challenge through concept recognition and analysis. Our methodology involved using the OpenDMAP semantic parser with manually written rules. The original OpenDMAP system was updated for this challenge with a broad ontology defined for the events of interest, new linguistic patterns for those events, and specialized coordination handling. We achieved state-of-the-art precision for two of the three tasks, scoring the highest of 24 teams at precision of 71.81 on Task 1 and the highest of 6 teams at precision of 70.97 on Task 2. We provide a detailed analysis of the training data and show that a number of trigger words were ambiguous as to event type, even when their arguments are constrained by semantic class. The data is also shown to have a number of missing annotations. Analysis of a sampling of the comparatively small number of false positives returned by our system shows that major causes of this type of error were failing to recognize second themes in two-theme events, failing to recognize events when they were the arguments to other events, failure to recognize nontheme arguments, and sentence segmentation errors. We show that specifically handling coordination had a small but important impact on the overall performance of the system. The OpenDMAP system and the rule set are available at http://bionlp.sourceforge.net.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.