Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …

Document-level relation extraction (RE) poses new challenges compared to its sentence-
level counterpart. One document commonly contains multiple entity pairs, and one entity pair …

Document-level relation extraction aims to extract relations among entities within a
document. Different from sentence-level relation extraction, it requires reasoning over …

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Abstract Entities, as the essential elements in relation extraction tasks, exhibit certain
structure. In this work, we formulate such entity structure as distinctive dependencies …

Document-level relation extraction is a complex human process that requires logical
inference to extract relationships between named entities in text. Existing approaches use …

Relation extraction (RE) aims to identify the semantic relations between named entities in
text. Recent years have witnessed it raised to the document level, which requires complex …

In document-level relation extraction (DocRE), graph structure is generally used to encode
relation information in the input document to classify the relation category between each …

Mineral exploration reports include not only a large number of geological profiles but also
geological text by offering valuable information and knowledge about the geological …

Document-level relation extraction requires inter-sentence reasoning capabilities to capture
local and global contextual information for multiple relational facts. To improve inter …