With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

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 …

Prompt-based methods have been successfully applied in sentence-level few-shot learning
tasks, mostly owing to the sophisticated design of templates and label words. However …

Labeling training data has become one of the major roadblocks to using machine learning.
Among various weak supervision paradigms, programmatic weak supervision (PWS) has …

Recent Weak Supervision (WS) approaches have had widespread success in easing the
bottleneck of labeling training data for machine learning by synthesizing labels from multiple …

Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …

Pre-trained models like BERT have achieved strong improvements on many natural
language processing (NLP) tasks, showing their great generalizability. The success of pre …

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many
natural language processing (NLP) tasks, but they still require excessive labeled data in the …

Neural sequence labeling is widely adopted for many Natural Language Processing (NLP)
tasks, such as Named Entity Recognition (NER) and slot tagging for dialog systems and …

We study the problem of training named entity recognition (NER) models using only distantly-
labeled data, which can be automatically obtained by matching entity mentions in the raw …