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      A Factoid Question Answering System for Vietnamese

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          Abstract

          In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.

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          Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics?

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            Question answering passage retrieval using dependency relations

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              Open Question Answering with Weakly Supervised Embedding Models

              Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.
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                Author and article information

                Journal
                01 March 2018
                Article
                1803.00712
                2bb85e9b-af21-490c-a93e-08d50e6b4a51

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                In the proceedings of the HQA'18 workshop, part of The Web Conference, Lyon, France
                cs.CL

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