Search Results for author:
Found 10 papers, 5 papers with code
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering
We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Learn to Not Link: Exploring NIL Prediction in Entity Linking
We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction.
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
We believe this is a critical bottleneck for realizing human-like cognition in PLMs.
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks
This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base.
Encoding Category Trees Into Word-Embeddings Using Geometric Approach
Experiment results also show that $n$-ball embeddings demonstrate surprisingly good performance in validating the category of unknown word.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases
Fine-grained entity typing aims at identifying the semantic type of an entity in KB.
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction
Measuring research impact and ranking academic achievement are important and challenging problems.
