[Elsnet-list] New publication about analysis and identification of negated bio-events in literature

anita bandrowski abandrowski at ucsd.edu
Thu Jan 17 20:57:56 CET 2013


Thanks for the interesting link, this is a very interesting paper.
I wonder how the algorithm does head to head against BioNOT?
https://neuinfo.org/mynif/search.php?q=%22Cerebellum%20neuron%22&t=indexable&nif=nlx_143912-1&b=0&r=20

Looks like this comparison is not made in the paper, but would be quite
interesting.

The negated statements in BioNOT are certainly fun to play with, but the
typical issues of false positives/negatives are present such as NO (as in
nitric oxide) being flagged as indicative of a negated statement. Is there
a place I can play with these data?

Best,
anita


On Thu, Jan 17, 2013 at 2:11 AM, Paul Thompson <
Paul.Thompson at manchester.ac.uk> wrote:

>
>   Raheel Nawaz, Paul Thompson and Sophia Ananiadou
>
> "Negated bio-events: analysis and identification"
>
> BMC Bioinformatics 2013, 14:14
>
> http://www.biomedcentral.com/1471-2105/14/14/
>
> doi:10.1186/1471-2105-14-14
>
>
>  Abstract
>
> ========
>
> Background
>
> ----------------
>
> Negation occurs frequently in scientific literature, especially in
> biomedical literature. It has previously been reported that around 13% of
> sentences found in biomedical research articles contain negation.
> Historically, the main motivation for identifying negated events has been
> to ensure their exclusion from lists of extracted interactions. However,
> recently, there has been a growing interest in negative results, which has
> resulted in negation detection being identified as a key challenge in
> biomedical relation extraction. In this article, we focus on the problem of
> identifying negated bio-events, given gold standard event annotations.
>
> Results
>
> ----------
>
> We have conducted a detailed analysis of three open access bio-event
> corpora containing negation information (i.e., GENIA Event, BioInfer and
> BioNLP'09 ST), and have identified the main types of negated bio-events. We
> have analysed the key aspects of a machine learning solution to the problem
> of detecting negated events, including selection of negation cues, feature
> engineering and the choice of learning algorithm. Combining the best
> solutions for each aspect of the problem, we propose a novel framework for
> the identification of negated bio-events. We have evaluated our system on
> each of the three open access corpora mentioned above. The performance of
> the system significantly surpasses the best results previously reported on
> the BioNLP'09 ST corpus, and achieves even better results on the GENIA
> Event and BioInfer corpora, both of which contain more varied and complex
> events.
>
> Conclusion
>
> ---------------
>
> Recently, in the field of biomedical text mining, the development and
> enhancement of event-based systems has received significant interest. The
> ability to identify negated events is a key performance element for these
> systems. We have conducted the first detailed study on the analysis and
> identification of negated bio-events. Our proposed framework can be
> integrated with state-of-the-art event extraction systems. The resulting
> systems will be able to extract bio-events with attached polarities from
> textual documents, which can serve as the foundation for more elaborate
> systems that are able to detect mutually contradicting bio-events.
>       --------
>
>  Paul Thompson
> Research Associate
> School of Computer Science
> National Centre for Text Mining
> Manchester Institute of Biotechnology
> University of Manchester
> 131 Princess Street
> Manchester
> M1 7DN
> UK
> Tel: 0161 306 3091
> http://personalpages.manchester.ac.uk/staff/Paul.Thompson/
>
>
>
>
>
>


-- 
Anita Bandrowski, Ph.D.
NIF Project Lead
UCSD 858-822-3629
http://neuinfo.org
9500 Gillman Dr.#0446
la Jolla, CA 92093-0446
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