Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

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

Abstract Recently, few-shot Named Entity Recognition (NER) has attracted significant
attention due to the high cost of obtaining high-quality labeled data. Decomposition-based …

Transforming text into a Semantic Link Network of Words (in short W-SLN) is an approach to
extracting the basic semantics from text for supporting natural language processing …

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 …

Open Information Extraction (OIE) aims to extract objective structured knowledge from
natural texts, which has attracted growing attention to build dedicated models with human …

The securities market carries complex financial interactions, providing challenges to its
prediction. To represent this complexity, researchers have utilized multi-source data, such as …

Information extraction from the scientific literature is one of the main techniques to transform
unstructured knowledge hidden in the text into structured data which can then be used for …

Cross-document event coreference resolution (CDECR) involves clustering event mentions
across multiple documents that refer to the same real-world events. Existing approaches …

Abstract Information Extraction (IE) is a core area of natural language processing focused on
identifying structured information, such as named entities and relationships, within plain text …