MindLab Neural Network Approach at BioASQ 6B
Andrés Rosso-Mateus, Fabio A. González, Manuel Montes-y-Gómez
Abstract
Biomedical Question Answering is concerned with the development of methods and systems that automatically find answers to natural language posed questions. In this work, we describe the system used in the BioASQ Challenge task 6b for document retrieval and snippet retrieval (with particular emphasis in this subtask). The proposed model makes use of semantic similarity patterns that are evaluated and measured by a convolutional neural network architecture. Subsequently, the snippet ranking performance is improved with a pseudo-relevance feedback approach in a later step. Based on the preliminary results, we reached the second position in snippet retrieval sub-task.- Anthology ID:
- W18-5305
- Volume:
- Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ioannis A. Kakadiaris, George Paliouras, Anastasia Krithara
- Venue:
- BioASQ
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 40–46
- Language:
- URL:
- https://aclanthology.org/W18-5305
- DOI:
- 10.18653/v1/W18-5305
- Cite (ACL):
- Andrés Rosso-Mateus, Fabio A. González, and Manuel Montes-y-Gómez. 2018. MindLab Neural Network Approach at BioASQ 6B. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering, pages 40–46, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- MindLab Neural Network Approach at BioASQ 6B (Rosso-Mateus et al., BioASQ 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W18-5305.pdf
- Data
- BioASQ