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

• URL: http://arxiv.org/abs/2510.10936v1
• Date: Mon, 13 Oct 2025 02:49:21 GMT
• Title: End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF: A Reproducibility Study
• Authors: Anirudh Ganesh, Jayavardhan Reddy,
• Abstract summary: We present a study of the state-of-the-art neural architecture for sequence labeling proposed by Ma and Hovycitemaend.<n>The original BiLSTM-CNN-CRF model combines character-level representations via Convolutional Neural Networks (CNNs), word-level context modeling through BiLSTMs, and structured prediction using Conditional Random Fields (CRFs)<n>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.
• Score: 1.7188280334580195
• License: http://creativecommons.org/licenses/by/4.0/
• 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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