匹配(统计)
计算机科学
模式识别(心理学)
突出
人工智能
卷积神经网络
图像(数学)
图层(电子)
新认知
人工神经网络
数学
字符识别
统计
有机化学
化学
作者
Liang Pang,Yanyan Lan,Jiafeng Guo,Jun Xu,Shengxian Wan,Xueqi Cheng
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2016-03-05
卷期号:30 (1)
被引量:419
标识
DOI:10.1609/aaai.v30i1.10341
摘要
Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.
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