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Your search keyword '"JIAFENG GUO"' showing total 94 results
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94 results on '"JIAFENG GUO"'

1. Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

Author

Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, and Xiaofei Zhu

Subjects
Information Retrieval, Language Models, Recommendation system, Electronic computers. Computer science, QA75.5-76.95
Abstract

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop’s outcomes, including the rethinking of IR’s core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.

Published
2023
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2. Aspect-based Sentiment Classification with Reinforced Dependency Graph

Author

Hongyang SONG, Xiaofei ZHU, and Jiafeng GUO

Subjects
natural language processing, aspect-based sentiment classification, deep reinforcement learning, graph attention network, dependency tree, Chemical engineering, TP155-156, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract

We proposed Reinforced Dependency Graph for Aspect-based Sentiment Classification (RDGSC), a reinforced dependency graph model for aspect-based sentiment classification. In this framework, we train a policy network using deep reinforcement learning and construct a reinforced dependency graph for aspect-based sentiment classification. The graph attention network is used to fuse the aspect-related information in the text over the reinforced dependency graph. Each contextual representation is given an aspect-related attention weight through a retrieve-based attention mechanism. A refined final representation is obtained for classification and calculating delayed reward to guide the policy network to updates. Extensive experiments were conducted on five publicly available datasets, the results show that our method is superior to all the baseline methods in two evaluation indicators Accuracy and F1.

Published
2022
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3. In Situ Toxicity Reduction and Food Safety Assessment of Pak Choi (Brassica campestris L.) in Cadmium-Contaminated Soil by Jointly Using Alkaline Passivators and Organic Fertilizer

Author

Wei Jiao, Zhi Li, Ruiping Li, Jiafeng Guo, Xiaoshu Hou, Xi Zhang, and Fangli Wang

Subjects
cadmium (Cd) toxicity, organic fertilizer, alkaline passivator, soil remediation, food safety, Chemical technology, TP1-1185
Abstract

An economical and effective method is still lacking for cadmium (Cd) toxicity reduction and food product safety improvement in soil–vegetable systems. Therefore, this study aimed to reduce the Cd toxicity to pak choi (Brassica campestris L.) by jointly using passivators and organic fertilizer, highlighting food products’ safety based on pot experiments. The results showed that compared with the control, organic fertilizer decreased the Cd content in edible parts and the soil’s available Cd by 48.4% and 20.9% on average, respectively, due to the 0.15-unit increases in soil pH. Once jointly applied with passivators, the decrements increased by 52.3–72.6% and 32.5–52.6% for the Cd content in edible parts and for the soil’s available Cd, respectively, while the pH increment increased by 0.15–0.46 units. Compared with the control, the transport factor of Cd was reduced by 61.9% and 50.9–55.0% when applying organic fertilizer alone and together with the passivators, respectively. The combination treatment of biochar and organic fertilizer performed the best in decreasing the Cd content in the edible parts and the soil’s available Cd. The combination treatment of fish bone meal and organic fertilizer induced the greatest increases in soil pH. The grey relational analysis results showed that the combination treatment of biochar and organic fertilizer performed the best in reducing the potential Cd pollution risk, thereby highlighting the vegetable food safety. This study provides a potential economical and effective technology for toxicity reduction and food safety in Cd-polluted soil.

Published
2023
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