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

Hao Zhang; Jae Ro; Richard Sproat

COLING 2020main13 citations

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

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%.

BibTeX

@inproceedings{zhang-etal-2020-semi,
    title = "Semi-supervised {URL} Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities",
    author = "Zhang, Hao  and
      Ro, Jae  and
      Sproat, Richard",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.411/",
    doi = "10.18653/v1/2020.coling-main.411",
    pages = "4667--4675"
}