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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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Abstract:
[EN] Passage retrieval is an important stage of question answering systems. Closed domain passage retrieval, e.g. biomedical passage retrieval presents additional challenges such as specialized terminology, more complex and elaborated queries, scarcity in the amount of available data, among others. However, closed domains also offer some advantages such as the availability of specialized structured information sources, e.g. ontologies and thesauri, that could be used to improve retrieval performance. This paper presents a novel approach for biomedical passage retrieval which is able to combine different information sources using a similarity matrix fusion strategy based on convolutional neural network architecture. The method was evaluated over the standard BioASQ dataset, a dataset specialized on biomedical question answering. The results show that the method is an effective strategy for biomedical passage retrieval able to outperform other state-of-the-art methods in this domain. ; COLCIENCIAS, REF. Agreement #727, 2016 provided financial as well as logistical and planning support. Mindlab research group (Universidad Nacional de Colombia sede Bogota) with the cooperation of INAOE (Instituto Nacional de Astrofisica, optica y Electronica) and Universitat Politecnica de Valencia wich also provided technical support for this work. The work of Paolo Rosso was carried out in the framework of the research project PROMETEO/2019/121. ; Rosso-Mateus, A.; Montes Gomez, M.; Rosso, P.; González, F. (2020). Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval. Journal of Intelligent & Fuzzy Systems. 39(2):2239-2248. https://doi.org/10.3233/JIFS-179887 ; S ; 2239 ; 2248 ; 39 ; 2 ; Humphreys, B. L., McCray, A. T., & Lindberg, D. A. B. (1993). The Unified Medical Language System. Methods of Information in Medicine, 32(04), 281-291. doi:10.1055/s-0038-1634945 ; Malakasiotis P. , Androutsopoulos I. , Bernadou A. , Chatzidiakou N. , Papaki E. , Constantopoulos P. , Pavlopoulos I. , Krithara A. , Almyrantis Y. and Polychronopoulos D. , et al., Challenge evaluation report 2 and roadmap, BioASQ Deliverable D 5 2014. ; National Institutes of Health. Pubmed baseline repository. ; Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., … Paliouras, G. (2015). An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinformatics, 16(1). doi:10.1186/s12859-015-0564-6 ; Wasim, M., Waqar, D., & Usman, D. (2017). A Survey of Datasets for Biomedical Question Answering Systems. International Journal of Advanced Computer Science and Applications, 8(7). doi:10.14569/ijacsa.2017.080767 ; Yin, W., Schütze, H., Xiang, B., & Zhou, B. (2016). ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Transactions of the Association for Computational Linguistics, 4, 259-272. doi:10.1162/tacl_a_00097
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Keyword:
Biomedical passage retrieval; Deep learning; LENGUAJES Y SISTEMAS INFORMATICOS; Neural networks; Question answering
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URL: https://doi.org/10.3233/JIFS-179887
http://hdl.handle.net/10251/166829
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Event-based summarization using a centrality-as-relevance model
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A logical representation of Arabic questions toward automatic passage extraction from the Web
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In: ISSN: 1381-2416 ; EISSN: 1572-8110 ; International Journal of Speech Technology ; https://hal.archives-ouvertes.fr/hal-01794688 ; International Journal of Speech Technology, Springer Verlag, 2017, 20 (2), pp.339 - 353. ⟨10.1007/s10772-017-9411-7⟩ (2017)
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An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.sorbonne-universite.fr/hal-01156600 ; BMC Bioinformatics, BioMed Central, 2015, 16 (1), pp.138. ⟨10.1186/s12859-015-0564-6⟩ (2015)
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LIMSI-CNRS@ CLEF 2014: Invalidating Answers for Multiple Choice Question Answering.
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SNUMedinfo at CLEFeHealth2013 task 3
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Voice-QA: Evaluating the Impact of Misrecognized Words on Passage Retrieval
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Finding answers to questions, in text collections or web, in open domain or specialty domains
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Voice-QA: evaluating the impact of misrecognized words on passage retrieval
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DCU at the NTCIR-9 spokendoc passage retrieval task
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Recuperación de pasajes multilingüe para la búsqueda de respuestas ; Multilingue passage retrieval for question answering
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Question Analysis and Answer Passage Retrieval for Opinion Question Answering Systems
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Scoring missing terms in information retrieval tasks
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Measurement, Experimentation
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A Semantic Approach to Boost Passage Retrieval Effectiveness for Question Answering
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