Named Entity Recognition

NER maps tokens or character spans to entity labels under a chosen ontology. Systems may use sequence tagging, span classification, transition-based prediction, rules or combinations of these. Boundary conventions determine whether titles, modifiers and punctuation belong to an entity. Some tasks allow nested or overlapping mentions, whereas particular model implementations may not. Context helps distinguish a person's name from an ordinary word or an organization from a location. A recognized span remains a mention in the text; entity linking assigns a canonical identity, and coreference resolution connects mentions that refer to the same thing. These are neighboring operations with separate training and evaluation requirements.

Write labeling rules with positive examples and difficult boundary cases. Review annotation disagreements and audit whether the selected model supports overlapping spans. Keep whole documents or entity-rich source families together across splits. Train or configure the recognizer, preserve character offsets and compare exact span-plus-type precision and recall by category. Inspect missed entities, partial spans and ordinary phrases falsely labeled as names. If the output feeds redaction or a database, evaluate that downstream effect as well. The deliverable is a recognizer and annotation contract whose labels and coordinates can be used consistently on unseen text.

NER depends on the label inventory and annotation conventions, so results from different schemes are not directly comparable. Unseen abbreviations and domain changes can reduce recall. Exact matching penalizes partial boundaries that may still be useful, while token accuracy can conceal missed rare entities. A detected organization name does not prove which organization it denotes. Distinguish recognition, linking and coreference, and verify source-text offsets before using predicted spans for redaction or structured records.