A deep learning question answering method over mixed closed domain information sources

digramas, tablas

Autores:: Rosso Mateus, Andrés Enrique

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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dc.title.eng.fl_str_mv	A deep learning question answering method over mixed closed domain information sources
dc.title.translated.spa.fl_str_mv	Un método de aprendizaje profundo para responder automáticamente a preguntas en dominio cerrado sobre fuentes de información mixta
title	A deep learning question answering method over mixed closed domain information sources
spellingShingle	A deep learning question answering method over mixed closed domain information sources 000 - Ciencias de la computación, información y obras generales Respuesta a preguntas, recuperación de pasajes, Deep Learning, Machine Learning, recuperación de información, biomédica, BioASQ. Respuesta a preguntas Recuperación de pasajes Recuperación de información Biomédica BioASQ Question answering Passage retrieval Deep Learning Machine Learning Biomedical information retrieval BioASQ Sistema de información médica Medical information systems Indexación automática Automatic indexing
title_short	A deep learning question answering method over mixed closed domain information sources
title_full	A deep learning question answering method over mixed closed domain information sources
title_fullStr	A deep learning question answering method over mixed closed domain information sources
title_full_unstemmed	A deep learning question answering method over mixed closed domain information sources
title_sort	A deep learning question answering method over mixed closed domain information sources
dc.creator.fl_str_mv	Rosso Mateus, Andrés Enrique
dc.contributor.advisor.none.fl_str_mv	González Osorio, Fabio Augusto Montes-y-Gómez, Manuel
dc.contributor.author.none.fl_str_mv	Rosso Mateus, Andrés Enrique
dc.contributor.researchgroup.spa.fl_str_mv	MindLab
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Respuesta a preguntas, recuperación de pasajes, Deep Learning, Machine Learning, recuperación de información, biomédica, BioASQ. Respuesta a preguntas Recuperación de pasajes Recuperación de información Biomédica BioASQ Question answering Passage retrieval Deep Learning Machine Learning Biomedical information retrieval BioASQ Sistema de información médica Medical information systems Indexación automática Automatic indexing
dc.subject.proposal.spa.fl_str_mv	Respuesta a preguntas, recuperación de pasajes, Deep Learning, Machine Learning, recuperación de información, biomédica, BioASQ. Respuesta a preguntas Recuperación de pasajes Recuperación de información Biomédica BioASQ
dc.subject.proposal.eng.fl_str_mv	Question answering Passage retrieval Deep Learning Machine Learning Biomedical information retrieval BioASQ
dc.subject.unesco.none.fl_str_mv	Sistema de información médica Medical information systems Indexación automática Automatic indexing
description	digramas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-05-25T21:57:38Z
dc.date.available.none.fl_str_mv	2021-05-25T21:57:38Z
dc.date.issued.none.fl_str_mv	2021-05-25
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79562
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79562 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2González Osorio, Fabio Augusto35912f60905ba6e179208c70e6024e80Montes-y-Gómez, Manuel253a4544c3c97e9f2385102bf8e5fe93600Rosso Mateus, Andrés Enriquef10b6eaa9d867fd07267efbec6a9fdb3MindLab2021-05-25T21:57:38Z2021-05-25T21:57:38Z2021-05-25https://repositorio.unal.edu.co/handle/unal/79562Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/digramas, tablasQuestion Answering (QA) is an active research area due to its usefulness in accessing the ever increasing amount of data. Information needs have led to the emergence of new information retrieval paradigms in which the user can easily access accurate information. QA methods allow to solve queries submitted by the user in natural language concisely and effectively, reducing the need for manual validation of large documents. In closed domains, such the biomedical one, these methods are relevant due to the large amount of specialized documents that make difficult the task of finding specific information as well as the usefulness of this information to support practice and research. In this research work, passage retrieval, which is often the final step in a question-answer system, was particularly addressed. This task evaluates the text fragments that make up the documents that may contain the answer to the question submitted by the user. This evaluation carries out semantic and sometimes syntactic checks that allow to deduce if the text passage is a valid answer, to finally return a ranked list of passages that have a higher probability of being an answer. In a closed domain, such as the biomedical domain, passage retrieval is particularly challenging due to the complexity of biomedical terminology and the heterogeneity of information sources. These challenges, along with others that will be detailed throughout the document, make it necessary to use other sources of information, such as semantic ones, which, when used in combination with textual sources, help to manage the complexity of language. On the other hand, the use of deep learning in this field has great interest and recently it has become increasingly popular as an important tool to solve the task of passage retrieval, however there are very few methods that merge the different modalities of information that in a domain like biomedicine offer obvious advantages. In this research work, different deep learning techniques were explored. In addition, several methods of information fusion were evaluated to take advantage of the complementarity of the modalities. The proposed methods were systematically evaluated in different open and closed domain data sets. Particularly in the biomedical domain the results were outstanding, surpassing the state of the art and demonstrating their effectiveness in the biggest global challenge for this particular task, BioASQ.La tarea de respuesta automática a preguntas (QA), es un área de investigación muy activa debido a su utilidad para acceder a la creciente cantidad de datos. Las necesidades de información han llevado a la aparición de nuevos paradigmas de recuperación de información en los que el usuario puede acceder fácilmente a información precisa. Los métodos de QA permiten resolver las consultas enviadas por el usuario en lenguaje natural de forma concisa y eficaz, reduciendo la necesidad de validación manual de grandes documentos. En dominios cerrados, como el biomédico, estos métodos son relevantes debido a la gran cantidad de documentos especializados que dificultan la tarea de encontrar información específica, así como la utilidad de esta información para apoyar la práctica y la investigación. En este trabajo de investigación se abordó especialmente la recuperación de pasajes, que suele ser el último paso de un sistema de QA. Esta tarea evalúa los fragmentos de texto que componen los documentos que pueden contener la respuesta a la pregunta presentada por el usuario. Esta evaluación realiza comprobaciones semánticas y a veces sintácticas que permiten deducir si el pasaje de texto es una respuesta válida, para finalmente devolver una lista clasificada de pasajes que tienen una mayor probabilidad de ser una respuesta. En un dominio cerrado, como el biomédico, la recuperación de pasajes es especialmente difícil debido a la complejidad de la terminología biomédica y a la heterogeneidad de las fuentes de información. Estos retos, junto con otros que se detallarán a lo largo del documento, hacen necesario el uso de otras fuentes de información, como las semánticas, que, utilizadas en combinación con las fuentes textuales, ayudan a gestionar la complejidad del lenguaje. Por otro lado, el uso del aprendizaje profundo en este campo tiene un gran interés y recientemente se ha popularizado como una importante herramienta para resolver la tarea de recuperación de pasajes, sin embargo existen muy pocos métodos que fusionen las diferentes modalidades de información que en un dominio como el biomédico ofrece evidentes ventajas. En este trabajo de investigación se exploraron diferentes técnicas de aprendizaje profundo. Además, se evaluaron varios métodos de fusión de información para aprovechar la complementariedad de las modalidades. Los métodos propuestos se evaluaron sistemáticamente en diferentes conjuntos de datos de dominio abierto y cerrado. Particularmente en el dominio biomédico los resultados fueron sobresalientes, superando el estado del arte y demostrando su efectividad en el mayor desafío global para esta tarea en particular, BioASQ.COLCIENCIAS, REF. Acuerdo 727, 2016DoctoradoDoctor en IngenieríaInteligencia artificial1 recurso en línea (114 páginas)application/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotáUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesRespuesta a preguntas, recuperación de pasajes, Deep Learning, Machine Learning, recuperación de información, biomédica, BioASQ.Respuesta a preguntasRecuperación de pasajesRecuperación de informaciónBiomédicaBioASQQuestion answeringPassage retrievalDeep LearningMachine LearningBiomedical information retrievalBioASQSistema de información médicaMedical information systemsIndexación automáticaAutomatic indexingA deep learning question answering method over mixed closed domain information sourcesUn método de aprendizaje profundo para responder automáticamente a preguntas en dominio cerrado sobre fuentes de información mixtaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAgichtein, Eugene ; Brill, Eric ; Dumais, Susan: Improving web search ranking by incorporating user behavior information. 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In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval ACM, 2015, S.LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79562/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL79958715.2021.pdf79958715.2021.pdfTesis de Doctorado en Ingeniería - Sistemas y computaciónapplication/pdf6786432https://repositorio.unal.edu.co/bitstream/unal/79562/2/79958715.2021.pdfac72dbffe46516945262ba1897a9af0fMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://repositorio.unal.edu.co/bitstream/unal/79562/3/license_rdf0175ea4a2d4caec4bbcc37e300941108MD53THUMBNAIL79958715.2021.pdf.jpg79958715.2021.pdf.jpgGenerated Thumbnailimage/jpeg4580https://repositorio.unal.edu.co/bitstream/unal/79562/4/79958715.2021.pdf.jpgd8ea9fdddb2d3137cb70ec929dca885cMD54unal/79562oai:repositorio.unal.edu.co:unal/795622024-07-19 23:32:41.946Repositorio Institucional Universidad 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