Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000
sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

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 …

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 …

Distantly supervised relation extraction is widely used to extract relational facts from text, but
suffers from noisy labels. Current relation extraction methods try to alleviate the noise by …

We propose a general joint representation learning framework for knowledge acquisition
(KA) on two tasks, knowledge graph completion (KGC) and relation extraction (RE) from text …

In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …

Sentence relation extraction aims to extract relational facts from sentences, which is an
important task in natural language processing field. Previous models rely on the manually …