Deep Word Association: A Flexible Chinese Word Association Method with Iterative Attention Mechanism
Word association is to predict the subsequent words and phrase, acting as a reminder to accelerate the text-editing process. Existing word association models can only predict the next word inflexibly through a given word vocabulary or a simply back-off N-gram language model. Herein, we propose a dee...
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Published in | Pattern Recognition and Computer Vision pp. 112 - 123 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Word association is to predict the subsequent words and phrase, acting as a reminder to accelerate the text-editing process. Existing word association models can only predict the next word inflexibly through a given word vocabulary or a simply back-off N-gram language model. Herein, we propose a deep word association system based on attention mechanism with the following contributions: (1) To the best of our knowledge, this is the first investigation of an attention-based recurrent neural network for word association. In the experiments, we provide a comprehensive study on the attention processes for the word association problem; (2) An novel approach, named DropContext, is proposed to solve the over-fitting problem during attention training procedure; (3) Compared with conventional vocabulary-based methods, our word association system can generate an arbitrary-length string of words that are reasonable; (4) Given information on different hierarchies, the proposed system can flexibly generate associated words accordingly. |
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ISBN: | 3030033376 9783030033378 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-03338-5_10 |