Update NER

src/content/docs/reference/preparation/ner.md

@@ -1,11 +1,36 @@
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 title: Named Entity Recognition
-description: A guide in my new Starlight docs site.
+description: A concise guide to Named Entity Recognition methods, applications, and challenges.
 ---
 
-Guides lead a user through a specific task they want to accomplish, often with a sequence of steps.
-Writing a good guide requires thinking about what your users are trying to do.
+Named Entity Recognition (NER) is the process of automatically identifying and categorizing key elements in text such as names of people, organizations, locations, and more.
+It is a foundational technique in natural language processing (NLP) that facilitates a range of applications including information extraction, content classification, and search optimization.
+
+## Traditional NER Methods
+
+Early NER systems relied on rule-based and statistical approaches.
+Tools like [SpaCy](https://spacy.io/) implement a combination of hand-crafted rules and machine learning algorithms to efficiently recognize entities in text.
+However, traditional NER models are typically effective only for a set of predefined entity types, limiting their flexibility.
+
+### GliNER: A Compact, Flexible Alternative
+
+In contrast to conventional systems, GliNER introduces a compact NER model designed to identify any type of entity.
+Leveraging a bidirectional transformer encoder, GliNER facilitates parallel entity extraction—an advantage over the slow sequential token generation characteristic of many large language models (LLMs).
+Comprehensive testing shows that GliNER outperforms both ChatGPT and fine-tuned LLMs in zero-shot evaluations on various NER benchmarks, addressing limitations in traditional models while maintaining resource efficiency.
+
+## NER with Large Language Models
+
+Modern large language models (LLMs) have introduced a paradigm shift in how NER tasks can be approached.
+LLMs can perform NER in a zero-shot or few-shot learning setting, extracting arbitrary entities through natural language instructions.
+This offers greater flexibility compared to traditional methods, although the size and cost of LLMs can be impractical in resource-limited scenarios.
+
+## Entity Linking and Entity Disambiguation
+
+Beyond merely identifying entities, many applications require associating them with specific, unique identifiers—a process known as entity linking.
+This often involves resolving ambiguities where the same name might refer to multiple real-world entities, a challenge known as entity disambiguation.
+High-level solutions typically integrate NER with external knowledge bases and context-aware algorithms to ensure that each recognized entity is accurately matched to its correct reference.
 
 ## Further reading
 
-- Read [about how-to guides](https://diataxis.fr/how-to-guides/) in the Diátaxis framework
+- [GliNER paper](https://arxiv.org/abs/2311.08526)
+- [SpaCy](https://spacy.io/)