Concept

Entity linking

In natural language processing, entity linking, also referred to as named-entity linking (NEL), named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris". Entity linking is different from named-entity recognition (NER) in that NER identifies the occurrence of a named entity in text but it does not identify which specific entity it is (see Differences from other techniques). In entity linking, words of interest (names of persons, locations and companies) are mapped from an input text to corresponding unique entities in a target knowledge base. Words of interest are called named entities (NEs), mentions, or surface forms. The target knowledge base depends on the intended application, but for entity linking systems intended to work on open-domain text it is common to use knowledge-bases derived from Wikipedia (such as Wikidata or DBpedia). In this case, each individual Wikipedia page is regarded as a separate entity. Entity linking techniques that map named entities to Wikipedia entities are also called wikification. Considering again the example sentence "Paris is the capital of France", the expected output of an entity linking system will be Paris and France. These uniform resource locators (URLs) can be used as unique uniform resource identifiers (URIs) for the entities in the knowledge base. Using a different knowledge base will return different URIs, but for knowledge bases built starting from Wikipedia there exist one-to-one URI mappings. In most cases, knowledge bases are manually built, but in applications where large text corpora are available, the knowledge base can be inferred automatically from the available text.

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https://en.wikipedia.org/wiki/Entity_linking

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Official source

https://en.wikipedia.org/wiki/Entity_linking

About this result

Related courses (28)

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Data Augmentation Strategies for Historical Named Entity Recognition

Historical Named Entity Recognition exhibits substantial performance degradation compared to modern text processing due to OCR errors, language evolution, and limited annotated training data. While various strategies have been explored, data augmentation techniques, despite proven effectiveness on modern NER benchmarks, remain unexplored for historical document processing to our knowledge. This thesis examines data augmentation strategies for historical NER through systematic error analysis and comparative

École Polytechnique Fédérale de Lausanne2026

Advancements in Multilingual Biomedical Natural Language Processing: exploring Large Language Models for Named Entity Recognition and Linking

Objective Named Entity Recognition (NER) and Biomedical Entity Linking (BEL) are essential for transforming unstructured Electronic Health Records (EHRs) into structured information. However, tools for these tasks are limited in non-English biomedical texts such as Dutch and Italian. This study investigates the use of prompt-based learning with Large Language Models (LLMs) to perform multilingual NER and BEL using minimal domainspecific data, while addressing annotation preservation during corpus translatio

openRxiv2026

Data Augmentation for Historical NER: A Systematic Comparison of Lexical and LLM-based Approaches

Named Entity Recognition (NER) on historical materials suffers significant performance degradation compared with modern text, owing to optical character recognition (OCR) errors, language evolution, and scarce annotated training data. Although various remedies have been explored to increase robustness and generalization, data augmentation techniques, despite their proven effectiveness on modern NER benchmarks, remain largely unexplored in the historical setting. This article investigates data augmentation s

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Related concepts (8)

Named-entity recognition

Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Most research on NER/NEE systems has been structured as taking an unannotated block of text, such as this one: Jim bought 300 shares of Acme Corp.

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

BabelNet

BabelNet is a multilingual lexicalized semantic network and ontology developed at the NLP group of the Sapienza University of Rome. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations.