When the Answer comes into Question in Question-Answering PDF
Preview When the Answer comes into Question in Question-Answering
UNIVERSIDADE DE LISBOA ´ INSTITUTO SUPERIOR TECNICO When the Answer comes into Question in Question-Answering Ana Cristina Bastos Mendes Supervisor: Doctor Maria Lu´ısa Torres Ribeiro Marques da Silva Coheur Co-Supervisor: Doctor G´abor Magyar Thesis approved in public session to obtain the PhD Degree in Information Systems and Computer Engineering Jury final classification: Pass with Merit Jury Chairperson: Chairman of the IST Scientific Board Members of the Committee: Doctor M´ario Jorge Costa Gaspar da Silva Doctor G´abor Magyar Doctor Anselmo Pen˜as Doctor Paulo Miguel Torres Duarte Quaresma Doctor P´avel Pereira Calado Doctor Maria Lu´ısa Torres Ribeiro Marques da Silva Coheur 2013 UNIVERSIDADE DE LISBOA ´ INSTITUTO SUPERIOR TECNICO When the Answer comes into Question in Question-Answering Ana Cristina Bastos Mendes Supervisor: Doctor Maria Lu´ısa Torres Ribeiro Marques da Silva Coheur Co-Supervisor: Doctor G´abor Magyar Thesis approved in public session to obtain the PhD Degree in Information Systems and Computer Engineering Jury final classification: Pass with Merit Jury Chairperson: Chairman of the IST Scientific Board Members of the Committee: Doctor M´ario Jorge Costa Gaspar da Silva, Professor Catedr´atico do Instituto Superior T´ecnico, da Universidade de Lisboa Doctor G´abor Magyar, Associate Professor, Budapest University of Technology and Economics, Hungary Doctor Anselmo Pen˜as, Associate Professor, Universidad Nacional de Educaci´on a Distancia, Escuela T´ecnica Superior de Ingenieria Inform´atica, Espanha Doctor Paulo Miguel Torres Duarte Quaresma, Professor Associado (com Agrega¸c˜ao) da Universidade de E´vora Doctor Pa´vel Pereira Calado, Professor Auxiliar do Instituto Superior T´ecnico, da Universidade de Lisboa Doctor Maria Lu´ısa Torres Ribeiro Marques da Silva Coheur, Professora Auxiliar do Instituto Superior T´ecnico, da Universidade de Lisboa Funding Institutions Funda¸c˜ao para a Ciˆencia e a Tecnologia 2013 Resumo Um sistema de resposta autom´atica a perguntas tem como objectivo devolver respostas cor- rectas a perguntas formuladas em l´ıngua natural. A arquitectura t´ıpica deste tipo de sistemas inclui um componente dedicado `a Extrac¸c˜ao da Resposta, no qual esta tese se foca. Este documento come¸ca por apresentar o sistema Just.Ask, desenvolvido no curso destes estudosdedoutoramentoequeconstituiabaselineeofocodetodasasexperiˆencias. Seguida- mente, aborda as tarefas de extrac¸c˜ao de respostas candidatas e de selec¸c˜ao da resposta final. Aprimeirautilizapadr˜oeslexico-sint´acticos,aprendidosautomaticamenteapartirdefontesde informac¸˜ao de larga escala, atrav´es de uma abordagem com supervis˜ao m´ınima. Na segunda, rela¸c˜oes semˆanticas – equivalˆencia e inclus˜ao – s˜ao detectados entre as respostas candidatas e usadas para melhor escolher a resposta final. Finalmente, ´e apresentada uma nova abordagem ao problema de responder automatica- mente a perguntas, tornando o Just.Ask no primeiro sistema, de acordo com o nosso melhor conhecimento, que usa as respostas correctas a perguntas anteriores para responder a pergun- tas futuras, utilizando os padr˜oes entretanto aprendidos. Nesta abordagem, o utilizador tem um papel fundamental, permitindo que o processo de aprendizagem se desencadeie: confirma a correc¸c˜ao da(s) resposta(s) dada(s) pelo sistema. Caso nenhuma das respostas devolvidas esteja correcta, poder´a ele pr´oprio fornecer a resposta correcta `a pergunta formulada. Abstract AQuestion-Answeringsystemaimsatreturningcorrectanswerstoquestionsposedinnatural language. The typical architecture of a Question-Answering system includes a component dedicated to Answer Extraction, which is the focus of this thesis. This thesis starts by presenting Just.Ask, the Question-Answering system developed during the course of these Ph.D. studies, that represents the baseline and the target of all the experiments. Afterwards, the tasks of candidate answer extraction and final answer selection are addressed. The former uses (lexico-syntactic) patterns, automatically learned from large- scale information sources using a minimally supervised approach. In the latter, semantic relations – equivalence and inclusion – are detected between the candidate answers and used to better choose the final answer. Finally, a new approach to the Question-Answering challenge is presented, making Just.Ask the first system, to the best of our knowledge, which uses the correct answers to past questions to answer future questions, employing the learned patterns. In this ap- proach, the user has a fundamental role that allows the learning process to be triggered: s/he confirms the correctness of the system’s answer(s). However, if none of the returned answers is correct, the user can provide the correct answer to the posed question. 7 Palavras-Chave Palavras-Chave Sistemas de Pergunta/Resposta Extrac¸ca˜o de Respostas Candidatas Padro˜es Lexico-sint´acticos Aprendizagem de Padr˜oes Unifica¸ca˜o Padr˜ao/Frase Estrat´egias de Relaxamento Selec¸ca˜o da Resposta Final Rela¸co˜es Semˆanticas Aprender a Responder Feedback do Utilizador
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