Structured Tender Entities Extraction from Complex Tables with Few-short Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AKY6Z7Q25" target="_blank" >RIV/00216208:11320/26:KY6Z7Q25 - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/anthology-files/anthology-files/pdf/regnlp/2025.regnlp-1.pdf#page=71" target="_blank" >https://aclanthology.org/anthology-files/anthology-files/pdf/regnlp/2025.regnlp-1.pdf#page=71</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Structured Tender Entities Extraction from Complex Tables with Few-short Learning

Popis výsledku v původním jazyce

Extracting structured text from complex tables in PDF tender documents remains a challenging task due to the loss of structural and positional information during the extraction process. AI-based models often require extensive training data, making development from scratch both tedious and time-consuming. Our research focuses on identifying tender entities in complex table formats within PDF documents. To address this, we propose a novel approach utilizing few-shot learning with large language models (LLMs) to restore the structure of extracted text. Additionally, handcrafted rules and regular expressions are employed for precise entity classification. To evaluate the robustness of LLMs with few-shot learning, we employ data-shuffling techniques. Our experiments show that current text extraction tools fail to deliver satisfactory results for complex table structures. However, the few-shot learning approach significantly enhances the structural integrity of extracted data and improves the accuracy of tender entity identification.

Název v anglickém jazyce

Structured Tender Entities Extraction from Complex Tables with Few-short Learning

Popis výsledku anglicky

Extracting structured text from complex tables in PDF tender documents remains a challenging task due to the loss of structural and positional information during the extraction process. AI-based models often require extensive training data, making development from scratch both tedious and time-consuming. Our research focuses on identifying tender entities in complex table formats within PDF documents. To address this, we propose a novel approach utilizing few-shot learning with large language models (LLMs) to restore the structure of extracted text. Additionally, handcrafted rules and regular expressions are employed for precise entity classification. To evaluate the robustness of LLMs with few-shot learning, we employ data-shuffling techniques. Our experiments show that current text extraction tools fail to deliver satisfactory results for complex table structures. However, the few-shot learning approach significantly enhances the structural integrity of extracted data and improves the accuracy of tender entity identification.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 1st Regulatory NLP Workshop

ISBN

979-8-89176-217-6

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

59-67

Název nakladatele

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Místo vydání

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Místo konání akce

Abu Dhabi, UAE

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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