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Compositional Coding Capsule Network with K-Means Routing for Text Classification

2018-10-22Code Available0· sign in to hype

Hao Ren, Hong Lu

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Abstract

Text classification is a challenging problem which aims to identify the category of texts. In the process of training, word embeddings occupy a large part of parameters. Under the limitation of limited computing resources, it indirectly limits the ability of subsequent network designs. In order to reduce the number of parameters, the compositional coding mechanism has been proposed recently. Based on this, this paper further explores compositional coding and proposes a compositional weighted coding method. And we apply capsule network to model the relationship between word embeddings, a new routing algorithm, which is based on k-means clustering theory, is proposed to fully mine the relationship between word embeddings. Combined with our compositional weighted coding method and the routing algorithm, we design a neural network for text classification. Experiments conducted on eight challenging text classification datasets show that the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Amazon Review FullCCCapsNetAccuracy60.95Unverified
Amazon Review PolarityCCCapsNetAccuracy94.96Unverified
Yelp Binary classificationCCCapsNetError3.52Unverified
Yelp Fine-grained classificationCCCapsNetError34.15Unverified

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