Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Lin, Nankai"'
Nested Named Entity Recognition (NNER) focuses on addressing overlapped entity recognition. Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER. Data augmentation is an effective approach to address
Externí odkaz:
http://arxiv.org/abs/2406.12779
Autor:
Lin, Nankai, Wu, Hongyan, Chen, Zhengming, Li, Zijian, Wang, Lianxi, Jiang, Shengyi, Zhou, Dong, Yang, Aimin
Hate speech on social media is ubiquitous but urgently controlled. Without detecting and mitigating the biases brought by hate speech, different types of ethical problems. While a number of datasets have been proposed to address the problem of hate s
Externí odkaz:
http://arxiv.org/abs/2406.04876
Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models,
Externí odkaz:
http://arxiv.org/abs/2304.01622
Grammatical error correction (GEC) is a challenging task of natural language processing techniques. While more attempts are being made in this approach for universal languages like English or Chinese, relatively little work has been done for low-reso
Externí odkaz:
http://arxiv.org/abs/2303.17367
Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classif
Externí odkaz:
http://arxiv.org/abs/2303.15697
Deep learning-based text classification models need abundant labeled data to obtain competitive performance. Unfortunately, annotating large-size corpus is time-consuming and laborious. To tackle this, multiple researches try to use data augmentation
Externí odkaz:
http://arxiv.org/abs/2302.00894
The effectiveness of contrastive learning technology in natural language processing tasks is yet to be explored and analyzed. How to construct positive and negative samples correctly and reasonably is the core challenge of contrastive learning. It is
Externí odkaz:
http://arxiv.org/abs/2212.00552
Chinese spelling check is a task to detect and correct spelling mistakes in Chinese text. Existing research aims to enhance the text representation and use multi-source information to improve the detection and correction capabilities of models, but d
Externí odkaz:
http://arxiv.org/abs/2210.13823
Chinese features prominently in the Chinese communities located in the nations of Malay Archipelago. In these countries, Chinese has undergone the process of adjustment to the local languages and cultures, which leads to the occurrence of a Chinese v
Externí odkaz:
http://arxiv.org/abs/2209.04611
As a fundamental task in natural language processing, Chinese Grammatical Error Correction (CGEC) has gradually received widespread attention and become a research hotspot. However, one obvious deficiency for the existing CGEC evaluation system is th
Externí odkaz:
http://arxiv.org/abs/2205.00217