TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition
| Autor: | YinRui Wang, Zhang Chen, Cong Liu, Dunlu Peng |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer Networks and Communications
business.industry Process (engineering) Computer science Deep learning 05 social sciences Text segmentation 02 engineering and technology computer.software_genre Theoretical Computer Science Domain (software engineering) Task (project management) Named-entity recognition 020204 information systems 0502 economics and business 0202 electrical engineering electronic engineering information engineering 050211 marketing Artificial intelligence Transfer of learning business computer Software Natural language processing Information Systems |
| Zdroj: | Information Systems Frontiers. 22:1291-1304 |
| ISSN: | 1572-9419 1387-3326 |
| DOI: | 10.1007/s10796-019-09932-y |
| Popis: | Most of the current research on Named Entity Recognition (NER) in the Chinese domain is based on the assumption that annotated data are adequate. However, in many scenarios, the sufficient amount of annotated data required for Chinese NER task is difficult to obtain, resulting in poor performance of machine learning methods. In view of this situation, this paper tries to excavate the information contained in the massive unlabeled raw text data and utilize it to enhance the performance of Chinese NER task. A deep learning model combined with Transfer Learning technique is proposed in this paper. This method can be leveraged in some domains where there is a large amount of unlabeled text data and a small amount of annotated data. The experiment results show that the proposed method performs well on different sized datasets, and this method also avoids errors that occur during the word segmentation process. We also evaluate the effect of transfer learning from different aspects through a series of experiments. |
| Databáze: | OpenAIRE |
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