Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Prediction

• URL: http://arxiv.org/abs/2601.16999v1
• Date: Tue, 13 Jan 2026 18:00:08 GMT
• Title: Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Prediction
• Authors: Matthew Singer, Srijan Sengupta, Karl Pazdernik,
• Abstract summary: We introduce a general framework for adapting sequence-labeling-based NER models to produce uncertainty-aware prediction sets.<n>Prediction sets are collections of full-sentence labelings guaranteed to contain the correct labeling with a user-specified confidence level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Named Entity Recognition (NER) serves as a foundational component in many natural language processing (NLP) pipelines. However, current NER models typically output a single predicted label sequence without any accompanying measure of uncertainty, leaving downstream applications vulnerable to cascading errors. In this paper, we introduce a general framework for adapting sequence-labeling-based NER models to produce uncertainty-aware prediction sets. These prediction sets are collections of full-sentence labelings that are guaranteed to contain the correct labeling with a user-specified confidence level. This approach serves a role analogous to confidence intervals in classical statistics by providing formal guarantees about the reliability of model predictions. Our method builds on conformal prediction, which offers finite-sample coverage guarantees under minimal assumptions. We design efficient nonconformity scoring functions to construct efficient, well-calibrated prediction sets that support both unconditional and class-conditional coverage. This framework accounts for heterogeneity across sentence length, language, entity type, and number of entities within a sentence. Empirical experiments on four NER models across three benchmark datasets demonstrate the broad applicability, validity, and efficiency of the proposed methods.

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