2024年国际计算机支持协同设计会议（The 2024 International Conference on Computer Supported Cooperative Work in Design，CSCWD2024）于5月8日至10日在天津举办。CSCWD 2024是一个涵盖计算机科学、信息技术和工程等领域的国际性学术会议，旨在促进计算机支持的合作工作和设计方面的研究与发展。本次大会主题“智能物联网与工业大数据”，包括协作技术的研究与开发，以及它们在工业和社会中的应用协作技术，会议邀请了梅宏、Andrew Kusiak、石光明、王兴伟等知名学者进行了相关的主题演讲。

实验室研究生李成、吴佳琪同学的论文《Promoting Named Entity Recognition with External Discriminator》以及郑威同学的论文《Integrating User Rules into Neural Text Inference》被CSCWD 2024录用，郑威同学在会议上对以上两篇论文做了ppt报告。

《Promoting Named Entity Recognition with External Discriminator》：

In this paper, we propose an NER promotion method formed as an external discriminator. It learns the patterns about the contextual entity usages from the extensive web data and thus it can check whether the recognized entity by an NER model is correct. Different with the current popular methods on introducing the entity knowledge by gazetteers or labeled data, it can be used as the additional part to work with any NER method for promoting its performance. We adopt

three widely adopted datasets for the empirical studies and the results show that our method significantly improves the NER performance. Besides, by using only a small proportion of labeled data, our method achieves a comparable performance against other models using the whole labeled data.

《Integrating User Rules into Neural Text Inference》：

Incorporating user-defined heuristic rules into neural text inference methods has the potential to align models with user intentions and domain knowledge, thereby improving interpretability. In this study, we introduce a novel rule pattern that includes both domain-specific keywords and the logical relationships between keywords, which can be defined by users. We propose an approach to integrate explicit rule-based reasoning with the semantic modeling capabilities of neural networks. Specifically, our method employs a parallel framework wherein a neural classifier is trained on labeled text data for prediction, while a Semantic-Logic Network (SLN) forms rule inference as a satisfiability problem. We use a Jensen-Shannon (JS) loss to ensure consistent predictions on both sides for mutual regularization. The experiment results show that our approach outperforms baseline methods. We also did ablation analysis on our method, it shows that the performance of both the SLN and the classifier contribute to the final results. Additionally, for the case that lacks explicit user rules, we propose a boosting method to automatically generate rules from labeled texts which is beneficial for text inference and improve the model performance.

CSCWD2024 主会场

郑威同学参加CSCWD会议

郑威同学做ppt报告

图文作者：郑威   责任编辑：孙宇清