# Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents

arXiv: https://arxiv.org/abs/2002.01861 · HTML: https://mlsanity.com/paper/2002.01861/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Customizing Contextualized Language Models forLegal Document Reviews](https://mlsanity.com/paper/2102.05757/) (2021)
- [Doc2Dict: Information Extraction as Text Generation](https://mlsanity.com/paper/2105.07510/) (2021)
- [Learning Representations on Logs for AIOps](https://mlsanity.com/paper/2308.11526/) (2023)
- [Jointly Learning Span Extraction and Sequence Labeling for Information Extraction from Business Documents](https://mlsanity.com/paper/2205.13434/) (2022)
- [LongFin: A Multimodal Document Understanding Model for Long Financial Domain Documents](https://mlsanity.com/paper/2401.15050/) (2024)
- [Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model](https://mlsanity.com/paper/2207.06839/) (2022)
- [Sustainable Digitalization of Business with Multi-Agent RAG and LLM](https://mlsanity.com/paper/2502.15700/) (2025)
- [Extracting Space Situational Awareness Events from News Text](https://mlsanity.com/paper/2201.05721/) (2022)

## Abstract (arXiv)

Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than 100 documents) are sufficient to achieve reasonable accuracy. We integrate our models into an end-to-end cloud platform that provides both an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect model outputs.