Search Results for author:
Found 11 papers, 10 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
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Automatic detection of multimodal misinformation has gained a widespread attention recently.
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.
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.
Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior Attribute
Then, many attack detection methods based on these datasets are proposed.
Re-Reading Improves Reasoning in Large Language Models
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i. e., \textbf{Re}-\textbf{Re}ading the question as input.
Wider and Deeper LLM Networks are Fairer LLM Evaluators
Each perspective corresponds to the role of a specific LLM neuron in the first layer.
Cross-modal Contrastive Learning for Multimodal Fake News Detection
An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations.
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning
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.
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
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.
