So far, named entity recognition (NER) has been involved with three major types, including
flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied …

Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Document-level Relation Extraction (DocRE) is a more challenging task compared to its
sentence-level counterpart. It aims to extract relations from multiple sentences at once. In …

Relation Extraction (RE) is a fundamental task of information extraction, which has attracted
a large amount of research attention. Previous studies focus on extracting the relations …

Identifying temporal and subevent relationships between different events (ie, event relation
extraction) is an important step towards event-centric natural language processing, which …

Event extraction (EE) is an essential task of information extraction, which aims to extract
structured event information from unstructured text. Most prior work focuses on extracting flat …

Multi-modal recommendation systems, which integrate diverse types of information, have
gained widespread attention in recent years. However, compared to traditional collaborative …

Recently, numerous efforts have continued to push up performance boundaries of document-
level relation extraction (DocRE) and have claimed significant progress in DocRE. In this …

Document-level relation extraction aims to infer complex semantic relations among entities
in an entire document. Compared with the sentence-level relation extraction, document-level …

Most previous studies on Structured Sentiment Analysis (SSA) have cast it as a problem of bi-
lexical dependency parsing, which cannot address issues of overlap and discontinuity …