Application of the Multi-label Residual Convolutional Neural Network text classifier using Content-Based Routing process
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Abstract (arXiv)
In this article, we will present an NLP application in text classifying process using the content-based router. The ultimate goal throughout this article is to predict the event described by a legal ad from the plain text of the ad. This problem is purely a supervised problem that will involve the use of NLP techniques and conventional modeling methodologies through the use of the Multi-label Residual Convolutional Neural Network for text classification. We will explain the approach put in place to solve the problem of classified ads, the difficulties encountered and the experimental results.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 19 Oct 2021.
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References · 2
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networkscited by 14
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