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

Hao Zhang; Jae Ro; Richard Sproat

doi:10.18653/v1/2020.coling-main.411

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arXiv · 2011.03138

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

Hao Zhang·Jae Ro·Richard Sproat

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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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Hao Zhang, Jae Ro, Richard Sproat. 2020-11-05. Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities. https://doi.org/10.18653/v1/2020.coling-main.411

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