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

dc.rights.licenseReconocimiento 4.0 Internacional
dc.contributor.advisorGonzález Osorio, Fabio Augusto
dc.contributor.advisorMontes-y-Gómez, Manuel
dc.contributor.authorRosso Mateus, Andrés Enrique
dc.date.accessioned2021-05-25T21:57:38Z
dc.date.available2021-05-25T21:57:38Z
dc.date.issued2021-05-25
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/79562
dc.descriptiondigramas, tablas
dc.description.abstractQuestion 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.
dc.description.abstractLa 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.
dc.description.sponsorshipCOLCIENCIAS, REF. Acuerdo 727, 2016
dc.format.extent1 recurso en línea (114 páginas)
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc000 - Ciencias de la computación, información y obras generales
dc.titleA deep learning question answering method over mixed closed domain information sources
dc.typeTrabajo de grado - Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Doctorado en Ingeniería - Sistemas y Computación
dc.contributor.researchgroupMindLab
dc.description.degreelevelDoctorado
dc.description.degreenameDoctor en Ingeniería
dc.description.researchareaInteligencia artificial
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrial
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.placeBogotá
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalRespuesta a preguntas, recuperación de pasajes, Deep Learning, Machine Learning, recuperación de información, biomédica, BioASQ.
dc.subject.proposalQuestion answering
dc.subject.proposalPassage retrieval
dc.subject.proposalDeep Learning
dc.subject.proposalMachine Learning
dc.subject.proposalBiomedical information retrieval
dc.subject.proposalBioASQ
dc.subject.proposalRespuesta a preguntas
dc.subject.proposalRecuperación de pasajes
dc.subject.proposalRecuperación de información
dc.subject.proposalBiomédica
dc.subject.proposalBioASQ
dc.subject.unescoSistema de información médica
dc.subject.unescoMedical information systems
dc.subject.unescoIndexación automática
dc.subject.unescoAutomatic indexing
dc.title.translatedUn método de aprendizaje profundo para responder automáticamente a preguntas en dominio cerrado sobre fuentes de información mixta
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TD
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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