Search Results for author:
Found 9 papers, 6 papers with code
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity Recogition
Cross-domain named entity recognition aims to improve performance in a target domain with shared knowledge from a well-studied source domain.
Cross-Domain Named Entity Recognition
Machine Reading Comprehension
+2
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning
Recent methods try to alleviate the CF problem by modifying models or replaying data, which may only remember the surface-level pattern of instructions and get confused on held-out tasks.
Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models
Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
HIP Network: Historical Information Passing Network for Extrapolation Reasoning on Temporal Knowledge Graph
In recent years, temporal knowledge graph (TKG) reasoning has received significant attention.
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning
Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text.
A Boundary Offset Prediction Network for Named Entity Recognition
By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs
Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage.
