Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities
This addresses a domain-specific problem for applications requiring accurate URL segmentation, but it is incremental as it adapts existing methods to a new context.
The paper tackled the problem of segmenting domain names into component words for applications like Text-to-Speech and web search, achieving an 85% sequence accuracy with a 33% improvement from pre-training on knowledge graph entities.
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%.