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of 154
pro vyhledávání: '"Gong, Jiaying"'
Autor:
Gong, Jiaying
Information extraction aims to automatically extract structured information from unstructured texts. Supervised information extraction requires large quantities of labeled training data, which is time-consuming and labor-intensive. This dissertation
Externí odkaz:
https://hdl.handle.net/10919/119210
Autor:
Gong, Jiaying, Eldardiry, Hoda
Publikováno v:
LREC-COLING 2024
The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are available for training. Existing few-shot relation extraction methods focus on uni-modal information such as te
Externí odkaz:
http://arxiv.org/abs/2403.00724
Autor:
Gong, Jiaying, Eldardiry, Hoda
E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values
Externí odkaz:
http://arxiv.org/abs/2402.08802
Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e-Commerce. Thus, we formulate AVE in multi-label fe
Externí odkaz:
http://arxiv.org/abs/2308.08413
Autor:
Zhang, Lu, Gong, Jiaying
Publikováno v:
In Journal of Air Transport Management September 2024 120
Autor:
Gong, Jiaying, Eldardiry, Hoda
Publikováno v:
LREC-COLING 2024
In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. Howe
Externí odkaz:
http://arxiv.org/abs/2112.04539
Autor:
Gong, Jiaying, Eldardiry, Hoda
We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data. The zero-shot learning approach mimics the way humans lear
Externí odkaz:
http://arxiv.org/abs/2011.07126
Autor:
Zhou, Jie, Li, Wenru, Guo, Minyi, Huang, Zicheng, Kong, Decan, Zhang, Fangling, Wang, Ling, Gong, Jiaying, Meng, Xiaochun
Publikováno v:
In European Journal of Radiology November 2023 168
Publikováno v:
In Psychiatry Research September 2023 327
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