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Semi-supervised URL Segmentation with Recurrent Neural NetworksPre-trained on Knowledge Graph Entities

2020-11-05 23:31:00
Hao Zhang, Jae Ro, Richard Sproat
arXiv_AI
               
Abstract

Breaking domain names such as openresearch into component words open and research is important for applications like Text-to-Speech synthesis and web search. We link this problem to the classic problem of Chinese word segmentation and show the effectiveness of a tagging model based on Recurrent Neural Networks (RNNs) using characters as input. To compensate for the lack of training data, we propose a pre-training method on concatenated entity names in a large knowledge database. Pre-training improves the model by 33% and brings the sequence accuracy to 85%.

Abstract (translated)

URL

https://arxiv.org/abs/2011.03138

PDF

https://arxiv.org/pdf/2011.03138.pdf

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