Search Results for author:
Found 16 papers, 12 papers with code
Learning Discriminative Representations for Open Relation Extraction with Instance Ranking and Label Calibration
In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction.
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction
Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage.
Cluster-aware Pseudo-Labeling for Supervised Open Relation Extraction
To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data.
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network
In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes.
Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
In this paper, we propose a novel graph-level automatic KG completion task called Triple Set Prediction (TSP) which assumes none of the elements in the missing triples is given.
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC).
MyGO: Discrete Modality Information as Fine-Grained Tokens for Multi-modal Knowledge Graph Completion
To overcome their inherent incompleteness, multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given MMKGs, leveraging both structural information from the triples and multi-modal information of the entities.
Making Large Language Models Perform Better in Knowledge Graph Completion
In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning.
Learning to Rank Utterances for Query-Focused Meeting Summarization
Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts.
Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization
Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism.
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
Learning to Construct 3D Building Wireframes from 3D Line Clouds
The network takes a line cloud as input , i. e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments.
Disentangled Ontology Embedding for Zero-shot Learning
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
Joint Topology-Preserving and Feature-Refinement Network for Curvilinear Structure Segmentation
To the best of our knowledge, this is the first work that jointly addresses topology preserving and feature refinement for CSS.
Cyclone intensity estimate with context-aware cyclegan
Deep learning approaches to cyclone intensity estimationhave recently shown promising results.
