Structured Tender Entities Extraction from Complex Tables with Few-short Learning (2025)

Abstract: 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.

## introduction tenders are formal requests for proposals or bids, typically issued by a company, organization, or government agency seeking goods, services, or works to be provided @xcite . in addition tender documents are the detailed specifications, terms, and conditions accompanying such requests, outlining the requirements and expectations for potential bidders. these documents ensure transparency, fairness, and accountability in the procurement process and are vital for decision in project management @xcite . the tender documents contains meaningful information that must be identified and extracted automatically to convert it into actionable knowledge to improve business decisions. generally, this information is available in an unstructured or semistructured format @xcite , which is understandable by humans but difficult to understand by machines because of a lack of documents or text structure, contextual understanding, ambiguity and noise, and limited domain knowledge. similarly, tender documents are large in size, often consist of over 100 pages each. manually extracting relevant information from such huge documents requires a lot of energy and time and is a labor-intensive task often prone to errors and inefficiencies. to address these challenges, natural language processing (nlp) based applications and techniques have emerged as a promising solution @xcite . this study addresses two key contributions crucial for automating tasks in the tender domain: i) structured text extraction from complex tables, a persistent challenge, is essential for tender documents as tables hold organized information vital for accurate analysis. losing the structure during extraction can result in misinterpretation, affecting decision-making in the tendering process @xcite . ii) tender named entities (tne) recognition and classification, including addresses, project details, dates, and personnel, are critical for retrieving relevant information, generating recommendations, and automating systems like chatbots and ir systems @xcite @xcite to address these challenges, we have introduced a novel approach that combines the capabilities of few-shot learning with large language models (llms) @xcite . our approach aims to reconstruct the text structure after extraction, thereby facilitating the accurate identification of tender elements. initially, we leverage existing pdf text extraction tools like pdfminor @xcite to extract raw text from tender documents, focusing on entities within tables. subsequently we identify the common terms that assist in document segmentation into header, body and footer. we discarded the body text to reduce the data dimensionality and improve entities categorization. further, header and footer text is concatenated, and few-shot learning with llm is leveraged to restructure it. after restructuring text, we employed hand crafted rules and regular expressions to automatically classify the entities into explicit categories. consequently, we achieve high accuracy towards tender entities extraction and classification through fewshort learning approach compared to without fewshort learning. furthermore, our study included comprehensive evaluation, comparison and limitation of existing tools utilized for structure text extraction from tender pdf tables. as a result we found that not a single tool provided desire performance towards structure text extraction from tender pdf tables because of the unstructured and dynamic structured of the tables. similarly, every tool have their own strength geared specific task. in the same way, in our study, we experimented and combined these tools in one place, utilized their explicit features, and developed our own algorithm to automatically extract the tender elements using rules and regular expressions. consequently, we achieved state-ofthe-art accuracy by integrating a few-shot learning approach. further, this study is structured as related studies are presented in section 2. continuing this, we presented proposed approach in section 3. additionally, in section 4 we presented experiments, evaluation and results of the proposed solution. finally, we conclude the study with limitation and future work in section 5. ## related studies over the years, a range of methodologies has been explored to enhance the accuracy of entity extraction from complex tender documents, including rule-based systems, machine learning (ml) approaches, and more recently, deep learning(dl) models. while these methods have demonstrated varying degrees of success, the heterogeneity and complexity of tender documents often lead to issues with text structure and format retention, making the task of extracting accurate and relevant entities even more challenging. in the study @xcite , a rules and self-learning approach using a conditional random field (crf) model has been introduced to automatically create and update the dictionary over time for recognizing the product entities in tender doc-uments. moreover, an ontology-based approach for information extraction from construction tender documents has been introduced to convert human-readable document structures into machinereadable formats @xcite . however, it is a challenging task to develop universal rules for the entire system, capture intricate semantic links between words, and manage named entities, especially in dynamic specialized domains. similarly, an incomplete dictionary can lead to a low recall while making a complete dictionary manually is tedious and time-consuming. to address the challenges with rule-based systems, state-of-the-art machine learning approaches have solved these challenges extensively in the tender domain for task such as named entities recognition @xcite . in the study @xcite an open information extraction for public administration (oie4pa) system was introduced for tender information retrieval from large databases. it extracts information based on triples (subject, predicate, object) found in documents, using tools like udpipe foot_0 and wikioie @xcite . in this process, two domain experts manually labeled these triples, which served as the training data for machine learning models such as support vector machines (svm), xgboost, and logistic regression (lr). however, machine learning models may not always reach peak performance on labeled data due to potential issues of over-fitting or under-fitting. this sometimes requires the use of intricate feature engineering approaches. conspicuously, dl is differs from the classical ml approaches by diminishing the demand for manually designing features such as bag-of-words or n-grams @xcite @xcite . as discussed previously, this study @xcite is further extended by implementing deep learning models such as bi-lstm, lstm, and conditional random field (crf) to extract the contract entities automatically @xcite . moreover, a dl-based approach is introduced @xcite to enhance the automatic identification of tender entities. the proposed architecture incorporates five main layers, for instance embedding layer using bert, bilstm input bert embedding as feature vector, feature fusion layer, attention layer, and crf layer. similarly, rnn-based architecture models have several constraints and lim-itations. these include the vanishing gradient problem during back-propagation through time, which makes it challenging for the model to learn longrange dependencies in sequential data. furthermore, rnns are sensitive to hyper-parameters such as learning rate, batch size, and sequence length. to address the limitations of existing methodologies, we introduce a large language model (llm)-based approach that incorporates few-shot learning for tender entity extraction and classification. by leveraging llms, we eliminate the need to build ai models from scratch, which typically requires vast training datasets and the involvement of domain experts is costly and time-consuming process. furthermore, we combine this ai-driven approach with rule-based methods to enhance accuracy in tender entity extraction and classification, while eliminating the need for ongoing lexicon and rule maintenance across the entire system. ## proposed approach this section outlines the methodology for structured tender text extraction and entity classification. we first used tools such as pdfminer (pdfminersix, 2024) to extract text from complex pdf tables. a text analyzer was then designed to perform preprocessing, keyword identification, and document segmentation into header, body, and footer. after discarding the body, we concatenated the header and footer. due to the loss of semantics, context, and sequence in extracted text, we employed a fewshot learning approach to restructure it. finally, regular expressions and rules were applied to classify tender entities into defined categories. ## experiments, evaluation and results we have utilized 30 commercial confidential tender documents, that can not be released. each document contains over 100 pages of information intended for architects, engineers, or project owners, submitted for approval by the contractor. the required tender entities are available in the form of complex table, which is difficult to extract accurately. in this section, we present the results of our pre-fewshot and post-fewshot learning approaches, which are combined with rules and regular expressions for structured text extraction from complex tables and the classification of tender entities. ## conclusion, limitation and future work structured text extraction from the complex table is an ongoing research challenge despite various ai tools and techniques. this study leverages llms in a few-shot learning environment to enhance tender entity classification from complex tables in pdf tender documents. we integrated text extraction tools, rules, and regular expressions with llms, and introduced text shuffling (50% and 100%) to assess llms capability in structured text extraction. after obtaining structured text, we applied handcrafted rules and regular expressions for precise entity classification. similarly, we assessed several text extraction tools towards structured data extraction, as a result we found that not a single tool provided desire performance. the experimental results demonstrate that after text extraction from tables employ few-shot learning significantly improves performance and accuracy, addressing the challenge of structured text extraction from complex tender tables. however this research has several limitations to be addressed in future work. first, the model's performance is heavily dependent on large datasets, especially in the pre-few-shot learning phase, where accuracy may decline with smaller datasets. additionally, the few-shot learning approach needs to be tested on datasets from other domains for a more robust evaluation. finally, exploring zero-shot and one-shot learning approaches would further validate our findings. in future, we aim to address the limitations of our study as discussed by expanding the dataset size and diversity to improve the accuracy and reliability of few-shot learning approach for structured data extraction and tender name entity recognition.