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- W2891910225 abstract "Question target classification, also known as answer type classification, turns out to be a key step for a high-performance QA system. The task has drawn more and more attention from academia and industry. This work proposes an artificial neural network architecture to classify questions according to their answer types. This architecture integrates several models. First, an embedding layer is trained using word2vec, an unsupervised language model, with a Chinese corpus from Wikipedia. Then, a bidirectional gated recurrent unit was concatenated on top of the embedding layer. A capsule layer and a softmax classifier is stacked on the architecture subsequently. Based on a SMP2017 dataset which contains over 3000 labeled Chinese questions, our method achieves an accuracy of 93.85% and F1-score of 93.68%, outperforming 6 baseline methods and demonstrating its effectiveness on question target classification." @default.
- W2891910225 created "2018-09-27" @default.
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- W2891910225 date "2018-01-01" @default.
- W2891910225 modified "2023-09-26" @default.
- W2891910225 title "Capsule-Based Bidirectional Gated Recurrent Unit Networks for Question Target Classification" @default.
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- W2891910225 doi "https://doi.org/10.1007/978-3-030-01012-6_6" @default.
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