no code implementations • EMNLP 2020 • Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.
Ranked #1 on
Extractive Text Summarization
on CNN / Daily Mail
no code implementations • 20 Jan 2025 • XiaoDong Li, Hengzhu Tang, Jiawei Sheng, Xinghua Zhang, Li Gao, Suqi Cheng, Dawei Yin, Tingwen Liu
To this end, we explore to utilize the explicit information injection capability of DMs for user preference integration and propose a Preference-Guided Diffusion Model for CDR to cold-start users, termed as DMCDR.
no code implementations • 6 Jan 2024 • Qian Li, Lixin Su, Jiashu Zhao, Long Xia, Hengyi Cai, Suqi Cheng, Hengzhu Tang, Junfeng Wang, Dawei Yin
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
1 code implementation • 8 Jul 2021 • Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang
A temporal interaction network consists of a series of chronological interactions between users and items.
no code implementations • COLING 2020 • Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo
Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.
1 code implementation • 23 Jun 2020 • Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
no code implementations • 28 Mar 2020 • Hengzhu Tang, Yanan Cao, Zhen-Yu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on
Relation Extraction
on DocRED