Search Results for author: Jiawei Sheng

Found 16 papers, 7 papers with code

Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion

no code implementations7 Mar 2024 Qian Li, Shu Guo, Yinjia Chen, Cheng Ji, Jiawei Sheng, JianXin Li

Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution.

Few-Shot Learning Knowledge Graph Completion

CDRNP: Cross-Domain Recommendation to Cold-Start Users via Neural Process

no code implementations23 Jan 2024 XiaoDong Li, Jiawei Sheng, Jiangxia Cao, Wenyuan Zhang, Quangang Li, Tingwen Liu

Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.


Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction

no code implementations4 Aug 2023 Shiyao Cui, Xin Cong, Jiawei Sheng, Xuebin Wang, Tingwen Liu, Jinqiao Shi

In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.

Contrastive Cross-Domain Sequential Recommendation

1 code implementation8 Apr 2023 Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, Bin Wang

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.

Sequential Recommendation

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

no code implementations5 Apr 2023 Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng, Quangang Li, Tingwen Liu, Jinqiao Shi

For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.

named-entity-recognition Named Entity Recognition +3

Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment

no code implementations4 Apr 2023 Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, JianXin Li

In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.

Attribute Graph Representation Learning +3

Event Causality Extraction with Event Argument Correlations

1 code implementation COLING 2022 Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi

Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.

Event Causality Identification

Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

no code implementations15 Nov 2022 Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie

To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.

Event Detection Semantic Similarity +2

Relation-Guided Few-Shot Relational Triple Extraction

1 code implementation SIGIR 2022 Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang

To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.

Relation RTE +1

Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck

1 code implementation31 Mar 2022 Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, Bin Wang

As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.

Recommendation Systems

Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning

1 code implementation EMNLP 2021 Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Mengge Xue, Hongbo Xu

Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.

Denoising named-entity-recognition +2

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Event Extraction Graph Attention +2

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

1 code implementation EMNLP 2020 Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu

Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.

Knowledge Graph Completion Link Prediction

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