@inproceedings{rosso-mateus-etal-2018-mindlab,
title = "{M}ind{L}ab Neural Network Approach at {B}io{ASQ} 6{B}",
author = "Rosso-Mateus, Andr{\'e}s and
Gonz{\'a}lez, Fabio A. and
Montes-y-G{\'o}mez, Manuel",
editor = "Kakadiaris, Ioannis A. and
Paliouras, George and
Krithara, Anastasia",
booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5305",
doi = "10.18653/v1/W18-5305",
pages = "40--46",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rosso-mateus-etal-2018-mindlab">
<titleInfo>
<title>MindLab Neural Network Approach at BioASQ 6B</title>
</titleInfo>
<name type="personal">
<namePart type="given">Andrés</namePart>
<namePart type="family">Rosso-Mateus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fabio</namePart>
<namePart type="given">A</namePart>
<namePart type="family">González</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Manuel</namePart>
<namePart type="family">Montes-y-Gómez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ioannis</namePart>
<namePart type="given">A</namePart>
<namePart type="family">Kakadiaris</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">George</namePart>
<namePart type="family">Paliouras</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anastasia</namePart>
<namePart type="family">Krithara</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">rosso-mateus-etal-2018-mindlab</identifier>
<identifier type="doi">10.18653/v1/W18-5305</identifier>
<location>
<url>https://aclanthology.org/W18-5305</url>
</location>
<part>
<date>2018-11</date>
<extent unit="page">
<start>40</start>
<end>46</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MindLab Neural Network Approach at BioASQ 6B
%A Rosso-Mateus, Andrés
%A González, Fabio A.
%A Montes-y-Gómez, Manuel
%Y Kakadiaris, Ioannis A.
%Y Paliouras, George
%Y Krithara, Anastasia
%S Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rosso-mateus-etal-2018-mindlab
%X 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.
%R 10.18653/v1/W18-5305
%U https://aclanthology.org/W18-5305
%U https://doi.org/10.18653/v1/W18-5305
%P 40-46
Markdown (Informal)
[MindLab Neural Network Approach at BioASQ 6B](https://aclanthology.org/W18-5305) (Rosso-Mateus et al., BioASQ 2018)
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.