- 著者
- Kenichiro Yaita Yohei Sakamoto Kazuhiko Nakaharai Yukihiro Yoshimura Natsuo Tachikawa
- 出版者
- 日本プライマリ・ケア連合学会
- 雑誌
- General Medicine (ISSN:13460072)
- 巻号頁・発行日
- vol.15, no.1, pp.68-71, 2014-06-20 (Released:2014-06-27)
- 参考文献数
- 12
- 被引用文献数
- 1 2
A 58-year-old female was admitted due to severe sepsis and multi-organ failure with a fulminant purpuric rash. Meropenem, vancomycin and levofloxacin were administered, although no focus of infection was detected. However, computed tomography revealed a profoundly hypoplastic spleen, and a blood smear detected Howell-Jolly bodies. Blood cultures grew Streptococcus pneumoniae (serotype 22F) three hours after admission. The patient was finally diagnosed as overwhelming pneumococcal sepsis with hyposplenism precipitated by splenic hypoplasia. Clinicians should pay attention to the splenic size and Howell-Jolly bodies in cases of sepsis of unknown origin.
- 著者
- Shohei Higashiyama Masao Utiyama Eiichiro Sumita Masao Ideuchi Yoshiaki Oida Yohei Sakamoto Isaac Okada Yuji Matsumoto
- 出版者
- The Association for Natural Language Processing
- 雑誌
- 自然言語処理 (ISSN:13407619)
- 巻号頁・発行日
- vol.27, no.3, pp.499-530, 2020-09-15 (Released:2020-12-15)
- 参考文献数
- 54
- 被引用文献数
- 2
Although limited effort has been devoted to exploring neural models in Japanese word segmentation, much effort has been actively applied to Chinese word segmentation because of the ability to minimize effort in feature engineering. In this work, we propose a character-based neural model that makes joint use of word information useful for disambiguating word boundaries. For each character in a sentence, our model uses an attention mechanism to estimate the importance of multiple candidate words that contain the character. Experimental results show that learning attention to proper words leads to accurate segmentations and that our model achieves better performance than existing statistical and neural models on both in-domain and cross-domain Japanese word segmentation datasets.