SeeDev Binary Event Extraction using SVMs and a Rich Feature Set

Autor:	Gitansh Khirbat, Kotagiri Ramamohanarao, Nagesh C. Panyam, Trevor Cohn, Karin Verspoor
Rok vydání:	2016
Předmět:	business.industry Event (computing) Computer science Extraction (chemistry) Binary number computer.software_genre Machine learning Task (project management) Support vector machine Set (abstract data type) Kernel (linear algebra) Data mining Artificial intelligence business computer Natural language
Zdroj:	BioNLP (Shared Task)
DOI:	10.18653/v1/w16-3010
Popis:	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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6dd7744311e4e589aae32b9517c42c9f https://doi.org/10.18653/v1/w16-3010 Zobrazit plný text záznamu