Yingfei Sun


ECO v1: Towards Event-Centric Opinion Mining
Ruoxi Xu | Hongyu Lin | Meng Liao | Xianpei Han | Jin Xu | Wei Tan | Yingfei Sun | Le Sun
Findings of the Association for Computational Linguistics: ACL 2022

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.


NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Canjia Li | Yingfei Sun | Ben He | Le Wang | Kai Hui | Andrew Yates | Le Sun | Jungang Xu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework that can be used with existing neural IR models by embedding different neural models as building blocks. Extensive experiments on two standard test collections confirm the effectiveness of the proposed NPRF framework in improving the performance of two state-of-the-art neural IR models.