Text Annotation for NER and Classification

Named entity recognition annotation involves marking specific spans of text, such as a person's name, a company, a date or a monetary amount, with start and end positions and a category label. Classification annotation instead assigns one or more labels to an entire piece of text, such as a whole email being labeled 'spam' or a whole review being labeled 'negative'. Both rely on a clearly defined labeling schema written before annotation begins, since ambiguity in the schema, such as whether 'the University of Texas' should be tagged as one organization entity or split into a location and an institution, causes annotators to disagree and produces noisy training data. A recurring technical challenge is tokenization: models process text as tokens, which may split a word like 'COVID-19' into multiple pieces, so entity boundaries marked by a human at the character level must be carefully aligned to the token boundaries the model actually sees, or the entity's label gets misapplied to only part of it. Overlapping and nested entities are another common difficulty; a phrase like 'Bank of America Tower' might need one entity for the building and a nested entity for the company name inside it, which many simple annotation formats cannot represent without a specific nested-entity or span-based schema. A widespread misconception is that classification is simpler or requires less schema work than NER; in practice, ambiguous document-level categories, such as separating 'complaint' from 'feedback', often generate as much annotator disagreement as span-level entity boundaries do. Inter-annotator agreement metrics can help identify where people labeling the same text disagree. The appropriate metric depends on the task, such as document labels versus span boundaries; Cohen's kappa is one chance-corrected option for suitable categorical judgments, not a universal measure for every NER setup. Low agreement calls for investigation of the schema, instructions, annotator calibration, and the metric before deciding what to revise.