Named Entity Linking for Cross-Document Information Extraction

Abstract

Named Entity Linking (NEL) contributes to the greater Cross-Document Information Extraction (CDIE) by clearing up any conflicts linked to text entity references. This task involves identifying mentions (such as persons, organizations, and places) in unstructured text and connecting them to a knowledge base entry in the appropriate context, ensuring that information from various documents is interpreted uniformly and consistently across all relevant contexts. Improved NEL supports the integration, comparison, and aggregation of data, thereby broadening the scope and accuracy of information extraction across multiple documents for event tracking, constructing knowledge graphs, and entity-centric search. However, entity overlapping, context variations, and knowledge bases redundancy remain persistent problems, especially in cross-domain or low-resource situations. Recent improvements focus on using advanced deep learning models, contextual embeddings, and graph-based methods to increase accuracy and scale of links. This abstract provides a concise overview of NEL within the scope of CDIE, discusses key issues, and outlines prediction solutions that challenge the level of automated understanding of documents as text. Enhanced NEL approaches not only augment the accuracy of subsequent CDIE tasks but also aid in knowledge sifting and automated reasoning over extensive text collections.