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- W2912081728 abstract "Current Question Answering (QA) systems have been significantly advanced in demonstratingfiner abilities to answer simple factoid and list questions. Such questions are easierto process as they require small snippets of texts as the answers. However, there isa category of questions that represents a more complex information need, which cannotbe satisfied easily by simply extracting a single entity or a single sentence. For example,the question: “How was Japan affected by the earthquake?” suggests that the inquirer islooking for information in the context of a wider perspective. We call these “complex questions”and focus on the task of answering them with the intention to minimize the existinggaps in the literature.The major limitation of the available search and QA systems is that they lack a way ofmeasuring whether a user is satisfied with the information provided. This was our motivationto propose a reinforcement learning formulation to the complex question answeringproblem. Next, we presented an integer linear programming formulation where sentencecompression models were applied for the query-focused multi-document summarizationtask in order to investigate if sentence compression improves the overall performance.Both compression and summarization were considered as global optimization problems.We also investigated the impact of syntactic and semantic information in a graph-basedrandom walk method for answering complex questions. Decomposing a complex questioninto a series of simple questions and then reusing the techniques developed for answeringsimple questions is an effective means of answering complex questions. We proposed asupervised approach for automatically learning good decompositions of complex questionsin this work. A complex question often asks about a topic of user’s interest. Therefore, theproblem of complex question decomposition closely relates to the problem of topic to questiongeneration. We addressed this challenge and proposed a topic to question generationapproach to enhance the scope of our problem domain." @default.
- W2912081728 created "2019-02-21" @default.
- W2912081728 creator A5064650291 @default.
- W2912081728 date "2013-01-01" @default.
- W2912081728 modified "2023-09-26" @default.
- W2912081728 title "Complex question answering : minimizing the gaps and beyond" @default.
- W2912081728 hasPublicationYear "2013" @default.
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