Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Pre-trained language models have attracted increasing attention in the biomedical domain,
inspired by their great success in the general natural language domain. Among the two main …

Named entity recognition (NER) task aims at identifying entities from a piece of text that
belong to predefined semantic types such as person, location, organization, etc. The state-of …

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a
document. Traditional ECEF aims to extract the cause based on a given emotion while …

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 …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

The challenge of information extraction (IE) lies in the diversity of label schemas and the
heterogeneity of structures. Traditional methods require task-specific model design and rely …

The mainstream method of end-to-end relation extraction is to jointly extract entities and
relations by sharing span representation, which, however, may cause feature conflict. The …

In this paper, we propose a novel method for joint entity and relation extraction from
unstructured text by framing it as a conditional sequence generation problem. In contrast to …

Abstract Aspect Sentiment Triplet Extraction (ASTE) has become an emerging task in
sentiment analysis research, aiming to extract triplets of the aspect term, its corresponding …