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Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Named entity recognition (NER) is one of the core application tasks of natural language processing. Traditional and deep NER methods rely heavily on a large amount of labeled training data with the same distribution, and the portability of the model is very poor. However, data are small and personalized in practical applications, and it is very difficult to collect enough training data. Transfer learning is used in NER, the data and model of the source domain are utilized to complete the target task model construction, increase the amount of labeled data in the target domain and reduce the demand for the amount of labeled data of the target model. It has a very good effect in dealing with the task of low- resource NER. Firstly, the method and difficulty of NER and the method of transfer learning are summarized. Then, transfer learning methods applied to NER including data-based transfer learning, model-based transfer learning and adversarial transfer learning in recent years are comprehensively reviewed, and adversarial transfer learning methods are mainly described. Finally, this paper further expounds the current problems and looks forward to the future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)