A Comparative Study of NER Methods for Ownership Structure Extraction from M&A Due Diligence Documents

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Published: 2026-01-18
Keywords:
named entity recognition, due diligence automation, ownership structure extraction, legal document processing

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Hanfei Zhang
Law, Emory University School of Law, Atlanta, GA, USA

Abstract

Cross-border mergers and acquisitions require efficient extraction of ownership structures from due diligence documentation. This study compares named-entity recognition methodologies for extracting equity structures from corporate governance documents. We construct an annotated dataset from authentic materials and evaluate six NER approaches spanning traditional sequence labeling (CRF, BiLSTM-CRF), general-purpose transformers (BERT, RoBERTa), and domain-adapted models (FinBERT-MRC, Legal-BERT). Legal-BERT achieves an overall F1 score of 87.3% while encountering challenges in multilingual entity names and nested ownership structures. Error analysis reveals three primary failure modes-cross-lingual recognition ambiguities, percentage-quantity confusion, and challenges in representing complex structures-providing actionable guidance for implementing automated equity analysis systems in time-sensitive M&A transactions.

Article Details

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Vol. 2 No. 1 (2026)

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Articles

How to Cite

A Comparative Study of NER Methods for Ownership Structure Extraction from M&A Due Diligence Documents. (2026). Journal of Sustainability, Policy, and Practice, 2(1), 71-86. https://schoalrx.com/index.php/jspp/article/view/78

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