In recent years, machine learning methods beyond the confines of conventional supervised learning have been used along with deep learning methods and intensively investigated. Fine-tuning, which improves the performance of one task by re-learning using the weights of a model learned for another task as initial values, is one such example. This paper proposes fine-tuning named entity recognition (NER) using part-of-speech tagging. The experiments revealed that fine-tuning improves the performance of NER. They also revealed that there was no performance improvement even if POS tag set included tags that corresponded to an NE tag, when there was a difference in definitions between these tags.
展开▼
