Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …

Overlapping entity relation extraction has received extensive research attention in recent
years. However, existing methods suffer from the limitation of long-distance dependencies …

Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA)
aims to significantly improve the classification performance and generalization capability of …

Relation classification (RC) is an essential task in natural language processing (NLP), which
extracts relationships of entity pairs in sentences of text. In the paper, a novel target attention …

Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via
collaborative distributed learning by aggregating multiple client models trained with local …

The joint extraction of entities and relations is an important task in natural language
processing, which aims to obtain all relational triples in plain text. However, few existing …

Relation extraction from dialogue text is an innovative task in natural language processing.
In addition to the general characteristics of general relation extraction from news or scientific …

With a focus on service interruptions occurring in optical networks, we propose a failure
prognostics scheme based on a bi-directional gated recurrent unit (BiGRU) from the …

Relation classification (RC) is an essential task in information extraction. The distance
supervision (DS) method can use many unlabeled data and solve the lack of training data …

With the rapid development of deep learning techniques, speech-based communication is
getting more practically to be embedded into smart devices such as Alexa echo, TV, Fridge …