Abstract Open Information Extraction (OpenIE) represents a crucial NLP task aimed at
deriving structured information from unstructured text, unrestricted by relation type or …

Open information extraction is an important NLP task that targets extracting structured
information from unstructured text without limitations on the relation type or the domain of the …

Current clustering-based open relation extraction (OpenRE) methods usually apply
clustering algorithms on top of pre-trained language models. However, this practice has …

Relation Extraction (RE) identifies relations between entities in text, typically relying on
supervised models that demand abundant high-quality data. Various approaches, including …

The sparsity of labelled data is an obstacle to the development of Relation Extraction (RE)
models and the completion of databases in various biomedical areas. While being of high …

Nowadays, the rapid development of mobile economy has promoted the flourishing of online
marketing campaigns, whose success greatly hinges on the efficient matching between user …

Tourism knowledge graphs lack cultural content, limiting their usefulness for cultural tourists.
This paper presents the development of a cultural perspective-based knowledge graph …

This article presents a study on the multi-class classification of job postings using machine
learning algorithms. With the growth of online job platforms, there has been an influx of labor …

In this article, we investigate the potential of synthetic resumes as a means for the rapid
generation of training data and their effectiveness in data augmentation, especially in …

Nowadays, data augmentation through synthetic data has been widely used in the field of
Grammatical Error Correction (GEC) to alleviate the problem of data scarcity. However …