Named Entity Recognition using Sliding Window Approach
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Computer Science > Computation and Language
arXiv:2609.29682 (cs)
[Submitted on 30 Aug 2026]
Title:Named Entity Recognition using Sliding Window Approach
Authors:Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Ravindra Murumkar, Raviraj Joshi
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Abstract: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 extend length while preserving contextual continuity, and Random Repeat, which concatenates distinct sequences to produce longer, heterogeneous inputs, each instantiated at three length levels, across several sliding-window configurations. The model retains a macro F1-score of up to 0.8902, with variation staying below one percentage point regardless of document length or construction strategy. Compared with the conventional non-windowed approach, the sliding-window pipeline avoids the boundary-fragmentation errors introduced by non-overlapping segmentation, yielding consistently higher and more stable document-level F1-scores.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.29682 [cs.CL] |
| (or arXiv:2609.29682v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29682
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Named Entity Recognition using Sliding Window Approach, by Hariom Ingle and 5 other authors
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