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%.- Anthology ID:
- 2020.coling-main.411
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4667–4675
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.411
- DOI:
- 10.18653/v1/2020.coling-main.411
- Cite (ACL):
- Hao Zhang, Jae Ro, and Richard Sproat. 2020. Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4667–4675, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities (Zhang et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.coling-main.411.pdf
- Code
- google-research-datasets/common-crawl-domain-names