Abstract Knowledge is a formal way of understanding the world, providing human-level
cognition and intelligence for the next-generation artificial intelligence (AI). An effective way …

Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …

Capsule networks emerged as a promising alternative to convolutional neural networks for
learning object-centric representations. The idea is to explicitly model part-whole hierarchies …

Deep convolutional neural networks, assisted by architectural design strategies, make
extensive use of data augmentation techniques and layers with a high number of feature …

Document-level relation extraction aims to extract relations among multiple entity pairs from
a document. Previously proposed graph-based or transformer-based models utilize the …

Log-linear models and more recently neural network models used for supervised relation
extraction requires substantial amounts of training data and time, limiting the portability to …

We propose a distance supervised relation extraction approach for long-tailed, imbalanced
data which is prevalent in real-world settings. Here, the challenge is to learn accurate" few …

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence
towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data …

Triple extraction is an essential task in information extraction for natural language
processing and knowledge graph construction. In this paper, we revisit the end-to-end triple …

Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG),
and it supports a variety of downstream natural language processing (NLP) tasks such as …