Learning Bayesian network using parse trees for extraction of protein-protein interaction

Pedro Nelson Shiguihara-Juárez, Alneu De Andrade Lopes

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

Extraction of protein-protein interactions from scientific papers is a relevant task in the biomedical field. Machine learning-based methods such as kernel-based represent the state-of-the-art in this task. Many efforts have focused on obtaining new types of kernels in order to employ syntactic information, such as parse trees, to extract interactions from sentences. These methods have reached the best performances on this task. Nevertheless, parse trees were not exploited by other machine learning-based methods such as Bayesian networks. The advantage of using Bayesian networks is that we can exploit the structure of the parse trees to learn the Bayesian network structure, i.e., the parse trees provide the random variables and also possible relations among them. Here we use syntactic relation as a causal dependence between variables. Hence, our proposed method learns a Bayesian network from parse trees. The evaluation was carried out over five protein-protein interaction benchmark corpora. Results show that our method is competitive in comparison with state-of-the-art methods.

Idioma originalInglés
Título de la publicación alojadaComputational Linguistics and Intelligent Text Processing - 14th International Conference, CICLing 2013, Proceedings
Páginas347-358
Número de páginas12
EdiciónPART 2
DOI
EstadoPublicada - 2013
Publicado de forma externa
Evento14th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013 - Samos, Grecia
Duración: 24 mar. 201330 mar. 2013

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 2
Volumen7817 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia14th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2013
País/TerritorioGrecia
CiudadSamos
Período24/03/1330/03/13

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