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Master Named Entity Recognition (NER) from rule-based systems to transformer-based models. Learn BIO tagging, CRF layers, SpaCy pipelines, evaluation metrics, and how to build custom NER systems for domain-specific applications.
Named Entity Recognition (NER) is the task of identifying and classifying named entities --- persons, organizations, locations, dates, and other proper nouns --- in unstructured text. It is one of the foundational tasks in information extraction and a critical building block for question answering, knowledge graph construction, and document understanding.
NER has evolved through several paradigm shifts. Early systems relied on handcrafted rules and gazetteers. Statistical methods like Hidden Markov Models and Conditional Random Fields (CRFs) introduced sequence labeling with learned transition constraints. The neural era brought BiLSTM-CRF architectures that combine learned feature extraction with structured prediction. Today, transformer-based models like BERT fine-tuned for token classification achieve state-of-the-art results across benchmarks.
This chapter covers the full NER pipeline: tagging schemes (BIO/BIOES), sequence labeling with CRF layers, practical NER with SpaCy, evaluation methodology, and building custom NER systems for specialized domains. You will implement each component and understand the trade-offs between approaches.
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From rule-based gazetteers to BiLSTM-CRF and transformer-based token classification.
Tagging schemes and structured prediction for coherent label sequences
The BIO/BIOES annotation scheme that encodes entity boundaries as token-level labels.
Conditional random fields for structured prediction with learned transition constraints.
Pretrained NER models, entity spans, and displaCy visualization for production pipelines.
Evaluation methodology and domain adaptation
Entity-level metrics, seqeval scoring, and partial matching strategies.
Annotation workflows, transformer fine-tuning, and few-shot approaches for domain-specific NER.
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