Options
Learning to Extract Protein-Protein Interactions using Distant Supervision
Thomas, Philippe; Solt, Illés; Klinger, Roman; u. a. (2011): Learning to Extract Protein-Protein Interactions using Distant Supervision, in: Bamberg: Otto-Friedrich-Universität, S. 25–32.
Faculty/Chair:
Author:
Publisher Information:
Year of publication:
2011
Pages:
Source/Other editions:
Proceedings of Workshop on Robust Unsupervised and Semisupervised Methods in Natural Language Processing / Chris Biemann, Anders Søgaard (Hg.). - Hissar, Bulgaria : Association for Computational Linguistics, 2011, S. 25–32.
Language:
English
Abstract:
Most relation extraction methods, especially in the domain of biology, rely on machine learning methods to classify a cooccurring pair of entities in a sentence to be related or not. Such an approach requires a training corpus, which involves expert annotation and is tedious, time-consuming, and expensive. We overcome this problem by the use of existing knowledge in structured databases to automatically generate a training corpus for protein-protein interactions. An extensive evaluation of different instance selection strategies is performed to maximize robustness on this presumably noisy resource. Successful strategies to consistently improve performance include a majority voting ensemble of classifiers trained on subsets of the training corpus and the use of knowledge bases consisting of proven non-interactions. Our best configured model built without manually annotated data shows very competitive results on several publicly available benchmark corpora
GND Keywords: ; ;
Maschinelles Lernen
Bioinformatik
Computerlinguistik
Keywords:
Protein-Protein Interactions
DDC Classification:
RVK Classification:
Peer Reviewed:
Yes:
International Distribution:
Yes:
Open Access Journal:
Yes:
Type:
Conferenceobject
Activation date:
August 23, 2024
Permalink
https://fis.uni-bamberg.de/handle/uniba/96475