As more and more Arabic texts emerged on the Internet, extracting important information
from these Arabic texts is especially useful. As a fundamental technology, Named entity …

Temporal reasoning is fundamental for large language models (LLMs) to comprehend the
world. Current temporal reasoning datasets are limited to questions about single or isolated …

As an important foundational task in the field of natural language processing, the Chinese
named entity recognition (NER) task has received widespread attention in recent years. Self …

Sharing knowledge between information extraction tasks has always been a challenge due
to the diverse data formats and task variations. Meanwhile, this divergence leads to …

Abstract Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the
data scarcity problem in NER by automatically generating training samples. Unfortunately …

Ontology alignment is essential for data integration and interoperability across multiple
applications across diverse disciplines. In recent decades, significant advancements have …

Integrating lexical knowledge in Chinese named entity recognition (NER) has been proven
effective. Among the existing methods, Flat-LAttice Transformer (FLAT) has achieved great …

Recently, word enhancement has become very popular for Chinese Named Entity
Recognition (NER), reducing segmentation errors and increasing the semantic and …

Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the
significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource …

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of
users through dialogues and generate relevant responses for item recommendations …