Search Results for author:
Found 12 papers, 8 papers with code
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive Learning
Knowledge-grounded dialogue (KGD) learns to generate an informative response based on a given dialogue context and external knowledge (\emph{e. g.}, knowledge graphs; KGs).
Dependency-aware Self-training for Entity Alignment
Entity Alignment (EA), which aims to detect entity mappings (i. e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion.
Guiding Neural Entity Alignment with Compatibility
Making compatible predictions thus should be one of the goals of training an EA model along with fitting the labelled data: this aspect however is neglected in current methods.
Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
Therefore, in this work, we propose a scalable GNN-based entity alignment approach to reduce the structure and alignment loss from three perspectives.
High-quality Task Division for Large-scale Entity Alignment
To include in the EA subtasks a high proportion of the potential mappings originally present in the large EA task, we devise a counterpart discovery method that exploits the locality principle of the EA task and the power of trained EA models.
Ensemble Semi-supervised Entity Alignment via Cycle-teaching
We also design a conflict resolution mechanism to resolve the alignment conflict when combining the new alignment of an aligner and that from its teacher.
Informed Multi-context Entity Alignment
Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources.
ActiveEA: Active Learning for Neural Entity Alignment
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion.
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects
Based on the given extrinsic and intrinsic analysis results, we note that compared to other theoretical-based techniques, the proposed hierarchical clustering approach can better group the users within the adaptive tree.
Cognitive-aware Short-text Understanding for Inferring Professions
Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications.
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble
The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security.
SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding
Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors.
