Zobrazeno 1 - 10
of 207
pro vyhledávání: '"Zheng Jiewen"'
Publikováno v:
Open Medicine, Vol 19, Iss 1, Pp 2823-34 (2024)
To investigate the risk factors associated with progressive kyphosis (PK) after percutaneous kyphoplasty (PKP) in osteoporotic vertebral compression fractures (OVCFs).
Externí odkaz:
https://doaj.org/article/f6364e6586fb4ec3b47cd2e0bc3be364
Autor:
Zheng, Jiewen, Chen, Ze
Sentence-level relation extraction aims to identify the relation between two entities for a given sentence. The existing works mostly focus on obtaining a better entity representation and adopting a multi-label classifier for relation extraction. A m
Externí odkaz:
http://arxiv.org/abs/2304.04935
Autor:
Zhang, Qi, Yang, Zijian, Huang, Yilun, Chen, Ze, Cai, Zijian, Wang, Kangxu, Zheng, Jiewen, He, Jiarong, Gao, Jin
In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, whe
Externí odkaz:
http://arxiv.org/abs/2302.07010
In this paper, we present an ensemble approach for the NL4Opt competition subtask 1(NER task). For this task, we first fine tune the pretrained language models based on the competition dataset. Then we adopt differential learning rates and adversaria
Externí odkaz:
http://arxiv.org/abs/2301.02459
This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning
Externí odkaz:
http://arxiv.org/abs/2211.03466
Autor:
Zhang, Qi, Yang, Zijian, Huang, Yilun, Chen, Ze, Cai, Zijian, Wang, Kangxu, Zheng, Jiewen, He, Jiarong, Gao, Jin
This paper mainly describes our winning solution (team name: www) to Amazon ESCI Challenge of KDD CUP 2022, which achieves a NDCG score of 0.9043 and wins the first place on task 1: the query-product ranking track. In this competition, participants a
Externí odkaz:
http://arxiv.org/abs/2208.02958
This study investigates whether the phonological features derived from the Featurally Underspecified Lexicon model can be applied in text-to-speech systems to generate native and non-native speech in English and Mandarin. We present a mapping of ARPA
Externí odkaz:
http://arxiv.org/abs/2204.07228
Autor:
Zheng, Jiewen, Guo, Dun, Zhang, Jingying, Zhang, Tongyao, Yang, Lei, Li, Bin, Lan, Jun, Ren, Yongxiang
Publikováno v:
In Environmental Research 15 October 2024 259
Publikováno v:
In Journal of Food Composition and Analysis March 2025 139
This study investigates whether phonological features can be applied in text-to-speech systems to generate native and non-native speech in English and Mandarin. We present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological feature
Externí odkaz:
http://arxiv.org/abs/2110.03609