Search Results for author: Xu Guo

Found 14 papers, 4 papers with code

基于多源知识融合的领域情感词典表示学习研究(Domain Sentiment Lexicon Representation Learning Based on Multi-source Knowledge Fusion)

no code implementations CCL 2022 Ruihua Qi, Jia Wei, Zhen Shao, Xu Guo, Heng Chen

“本文旨在解决领域情感词典构建任务中标注数据资源相对匮乏以及情感语义表示不充分问题, 通过多源数据领域差异计算联合权重, 融合先验情感知识和Fasttext词向量表示学习, 将情感语义知识映射到新的词向量空间, 从无标注数据中自动构建适应大数据多领域和多语言环境的领域情感词典。在中英文多领域公开数据集上的对比实验表明, 与情感词典方法和预训练词向量方法相比, 本文提出的多源知识融合的领域情感词典表示学习方法在实验数据集上的分类正确率均有明显提升, 并在多种算法、多语言、多领域和多数据集上具有较好的鲁棒性。本文还通过消融实验验证了所提出模型的各个模块在提升情感分类效果中的作用。”

Representation Learning

Gradient based Feature Attribution in Explainable AI: A Technical Review

no code implementations15 Mar 2024 Yongjie Wang, Tong Zhang, Xu Guo, Zhiqi Shen

Due to the lack of a rigorous definition of explainable AI (XAI), a plethora of research related to explainability, interpretability, and transparency has been developed to explain and analyze the model from various perspectives.

Autonomous Driving

Generative AI for Synthetic Data Generation: Methods, Challenges and the Future

no code implementations7 Mar 2024 Xu Guo, Yiqiang Chen

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI).

Synthetic Data Generation

Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction

no code implementations5 Dec 2023 Zilin Du, Haoxin Li, Xu Guo, Boyang Li

Comparing our method to direct training on synthetic data, we observed a significant improvement of 24. 06% F1 with synthetic text and 26. 42% F1 with synthetic images.

Relation Relation Extraction

Training Multimedia Event Extraction With Generated Images and Captions

no code implementations15 Jun 2023 Zilin Du, Yunxin Li, Xu Guo, Yidan Sun, Boyang Li

Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction.

Event Extraction Structured Prediction

On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey

no code implementations6 Nov 2022 Xu Guo, Han Yu

Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs).

Domain Adaptation Model Optimization

Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation

1 code implementation6 Oct 2022 Xu Guo, Boyang Li, Han Yu

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP tasks.

Domain Adaptation Language Modelling

A Generic Algorithm for Top-K On-Shelf Utility Mining

no code implementations27 Aug 2022 Jiahui Chen, Xu Guo, Wensheng Gan, Shichen Wan, Philip S. Yu

Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications.

Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection

1 code implementation NAACL 2021 Xu Guo, Boyang Li, Han Yu, Chunyan Miao

The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.

Meta-Learning Sarcasm Detection +1

Federated Learning for Personalized Humor Recognition

no code implementations3 Dec 2020 Xu Guo, Han Yu, Boyang Li, Hao Wang, Pengwei Xing, Siwei Feng, Zaiqing Nie, Chunyan Miao

In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).

Federated Learning Language Modelling

DIFER: Differentiable Automated Feature Engineering

1 code implementation17 Oct 2020 Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan, Yihua Huang

Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.

Automated Feature Engineering BIG-bench Machine Learning +1

False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation

1 code implementation27 Feb 2020 Lilun Du, Xu Guo, Wenguang Sun, Changliang Zou

We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence.

Methodology Statistics Theory Statistics Theory

Deep Learning Inversion of Electrical Resistivity Data

no code implementations10 Apr 2019 Bin Liu, Qian Guo, Shucai Li, Benchao Liu, Yuxiao Ren, Yonghao Pang, Xu Guo, Lanbo Liu, Peng Jiang

According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints, and depth weighting function together achieve the best performance.

Model Selection

Cannot find the paper you are looking for? You can Submit a new open access paper.