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Titlebook: Natural Language Processing and Chinese Computing; 6th CCF Internationa Xuanjing Huang,Jing Jiang,Yu Hong Conference proceedings 2018 Sprin

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樓主: 宗派
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發(fā)表于 2025-3-30 08:44:58 | 只看該作者
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發(fā)表于 2025-3-30 13:09:34 | 只看該作者
Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tagssentence-pair modeling, but ignore the peculiars of question-answer pairs. This paper proposes to model the interaction between question words and POS tags, as a special kind of information that is peculiar to question-answer pairs. Such information is integrated into a neural model for answer selec
53#
發(fā)表于 2025-3-30 20:10:50 | 只看該作者
A Deep Learning Way for Disease Name Representation and Normalizationthat dictionary lookup method couldn’t get a high accuracy on this task. Dnorm is the first machine learning approach for this task. It is not robust enough due to strong dependence on training dataset. In this article, we propose a deep learning way for disease name representation and normalization
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發(fā)表于 2025-3-30 22:47:22 | 只看該作者
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發(fā)表于 2025-3-31 01:35:05 | 只看該作者
Random Projections with Bayesian Priorsertain forms of distances or distance kernels such as Euclidean distances, inner products [.], and . distances [.] between high dimensional vectors are approximately preserved in this smaller dimensional subspace. Word vectors which are represented in a bag of words model can thus be projected down
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