Named Entity Recognition using Sliding Window Approach
Named Entity Recognition (NER) is a core NLP task, but transformer-based sentence-level models struggle with long documents because of fixed input-length limits: truncation drops content, and non-overlapping chunking fragments entities at segment boundaries. We introduce an inference-only pipeline that extends a frozen NER model, MahaNER-BERT, fine-tuned on the MahaNER corpus, to document-level prediction via overlapping sliding windows that are merged into a single annotation, without any retraining or architectural change. We evaluate the pipeline on six document-level corpora built from the MahaNER test set using two strategies: Normal Repeat, which duplicates sentence sequences to ext...
| Authors | Hariom Ingle, Ronit Ghode, Ishwari Gondkar et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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