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arXiv · 2510.10936

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study

Anirudh Ganesh·Jayavardhan Reddy
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Abstract

We present a reproducibility study of the state-of-the-art neural architecture for sequence labeling proposed by Ma and Hovy (2016)\cite{ma2016end}. The original BiLSTM-CNN-CRF model combines character-level representations via Convolutional Neural Networks (CNNs), word-level context modeling through Bi-directional Long Short-Term Memory networks (BiLSTMs), and structured prediction using Conditional Random Fields (CRFs). This end-to-end approach eliminates the need for hand-crafted features while achieving excellent performance on named entity recognition (NER) and part-of-speech (POS) tagging tasks. Our implementation successfully reproduces the key results, achieving 91.18\% F1-score on CoNLL-2003 NER and demonstrating the model's effectiveness across sequence labeling tasks. We provide a detailed analysis of the architecture components and release an open-source PyTorch implementation to facilitate further research.

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Anirudh Ganesh, Jayavardhan Reddy. 2025-10-13. End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study. https://arxiv.org/abs/2510.10936

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