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

• URL: http://arxiv.org/abs/2011.03138v1
• Date: Thu, 5 Nov 2020 23:31:00 GMT
• Title: Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
• Authors: Hao Zhang and Jae Ro and Richard Sproat
• Abstract summary: We show 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 synthesisd entity names in a large knowledge database.
• Score: 8.855143852360328
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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