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arXiv Machine Learning By Hariom Ingle, Ronit Ghode, Ishwari Gondkar, Jidnyasa Harad, Ravindra Murumkar, Raviraj Joshi

Named Entity Recognition using Sliding Window Approach

Read the original on arXiv Machine Learning →

The paper presents an inference-only pipeline that extends the frozen NER model MahaNER‑BERT to document‑level prediction using overlapping sliding windows, eliminating the need for retraining or architectural changes. The approach is evaluated on six document‑level corpora derived from the MahaNER test set, employing two repetition strategies (Normal Repeat and Random Repeat) at three length levels and various window configurations. Results show the model maintains a macro F1‑score of up to 0.8902 with minimal variation, outperforming non‑windowed methods by avoiding boundary‑fragmentation errors and achieving more stable document‑level performance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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