The goal of document-level relation extraction is to extract semantic information from multiple sentences within a document and identify the relations between entities across sentences. However, effectively representing the document's content and ...

The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the poor performance of pure "text-in, text-out" language models (LMs). To ...

Relation extraction methods are currently dominated by deep neural models, which capture complex statistical patterns while being brittle and vulnerable to perturbations in data and distribution. Explainability techniques offer a means for understanding ...

In educational data mining, concept prerequisite relations extraction determines which concepts need to be learned before learning another concept. It plays a crucial role in pedagogical practices, such as learning path planning and curriculum design. ...

Relation extraction is the task of extracting relationships from input text, where input can be a sentence, document, or multiple documents. This task has been popular for decades and is still of keen interest. Various techniques have been proposed to ...

Relation extraction (RE) methods have achieved striking performance when training and test data are independently and identically distributed (i.i.d). However, in real-world scenarios where RE models are trained to acquire knowledge in the wild, the ...

With the acceleration of the digital transformation in the judicial field, the need for automated processing and analysis of voluminous legal documents becomes increasingly imperative. Relationship extraction, a pivotal step in comprehending these ...

Prompt-tuning and instruction-tuning of language models have exhibited significant results in few-shot Natural Language Processing (NLP) tasks, such as Relation Extraction (RE), which involves identifying relationships between entities within a sentence. ...

Drug–drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public ...

Annotation of a natural language corpus not only facilitates researchers in extracting knowledge from it but also helps achieve deeper mining of the corpus. However, an annotated corpus in the humanities knowledge domain is lacking. In addition, the ...

In the financial domain, understanding the relationship between two entities helps in understanding financial texts. In this paper, we introduce the Mask One At a Time (MOAT) framework for detecting the relationship between financial entities. ...

Power marketing text data contains more specialized terms in specific fields, and there are multiple nested text entities, therefore the relationship between entity recognition is more difficult. In this paper, a graph convolution-based entity relation ...

Medical dialogue information extraction is an important but challenging task for Electronic Medical Records. Existing medical information extraction methods ignore the crucial information of sentence and multi-level dependency in dialogue, which limits ...

Extracting relational facts from multimodal data is a crucial task in the field of multimedia and knowledge graphs that feeds into widespread real-world applications. The emphasis of recent studies centers on recognizing relational facts in which both ...

In recent years, with the emergence of online learning platforms and e-learning resources, many documents are available for a particular topic. For a better learning experience, the learner often needs to know and learn first the prerequisite concepts ...

Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various ...

In the research field of relation extraction, a large amount of labeled training data can be quickly obtained through distant supervision. However, distant supervision will be inevitably accompanied by the problem of wrong labelling, and the noisy ...$\mathit{}$$\mathit{}$$\mathit{}$$$

Joint entity and relation extraction (RE) construct a framework for unifying entity recognition and relationship extraction, and the approach can exploit the dependencies between the two tasks to improve the performance of the task. However, the existing ...

A large volume of unstructured data, especially text data, is generated and exchanged daily. Consequently, the importance of extracting patterns and discovering knowledge from textual data is significantly increasing. As the task of automatically ...

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., ...