Advancing Community Directories: Leveraging LLM s for Automated Extraction in MARC Standard Venue Availability Notes (2024)

Abstract: This paper addresses the challenge of efficiently managing and accessing community service information, specifically focusing on venue hire details within the SAcommunity directory. By leveraging Large Language Models (LLMs), particularly the RoBERTa transformer model, we developed an automated system to extract and structure venue availability information according to MARC (Machine-Readable Cataloging) standards. Our approach involved fine-tuning the RoBERTa model on a dataset of community service descriptions, enabling it to identify and categorize key elements such as facility names, capacities, equipment availability, and accessibility features. The model was then applied to process unstructured text data from the SAcommunity database, automatically extracting relevant information and organizing it into standardized fields. The results demonstrate the effectiveness of this method in transforming free-text summaries into structured, MARC-compliant data. This automation not only significantly reduces the time and effort required for data entry and categorization but also enhances the accessibility and usability of community information.

## introduction in the realm of digital information management, the seamless transition between unstructured text and structured data remains a case of efficiency and utility. particularly within the context of event management where details range from facilities and capacities to rental fees and accommodations for the disabled, the need for sophisticated data extraction methods is paramount. this work proposes to enhance community directories by leveraging stateof-the-art deep learning models for automated data extraction. community directories are centralized databases or listings that provide information about local services, organizations, and resources available to residents within a specific community or region, in our case south australia. the work focuses on converting open-field free-text summaries of community service information into structured, marc (machine-readable cataloging) standardcompliant data elements by the library of congress (library of @xcite , specifically targeting "venue availability" for meeting rooms and facilities. we have chosen this aspect due to its high demand, as indicated by significant searches in google analytics for "venue hire". our strategy involves not only meeting the current demand but also laying the groundwork for creating truly closed fields in the future. we aim to address the gap in effectively utilizing unstructured text describing venue hire capabilities for sacommunity, a free online community service established in 1981 and supported by the government of south australia. the work involves extracting information from open fields, specifically focusing on the physical description fields (marc21 @xcite section of marc 21 community information library. the primary challenges in extracting structured venue hire information from unstructured text include: this study on automated extraction of venue availability information using a roberta-based model demonstrated promising outcomes. the model achieved a peak accuracy of 0.78 on the test set, with balanced precision and recall scores of approximately 0.65 and 0.70, respectively. the f1 score reached 0.65, indicating a good balance between precision and recall. these results suggest that the model effectively learned to extract and classify venue availability information from unstructured text, potentially streamlining the process of updating and maintaining community information directories. this research is critical because it tackles a prevalent issue in digital librarianship and information management: the efficient utilization of unstructured text. by developing a method to extract structured data from free-form text, the research supports better data management practices, improves accessibility, and enhances decision-making processes within community and event management sectors. it also contributes to the broader field of information science by integrating cutting-edge nlp technologies to solve real-world problems. ## related works recent advancements in nlp, particularly in named entity recognition (ner) and text classification, form the foundation of this research. transformer models like bert @xcite and roberta @xcite have shown significant potential in understanding context and extracting relevant information from text. for instance, @xcite provide a comprehensive survey across various domains, emphasizing the pivotal role of deep learning in enhancing ner capabilities. @xcite introduce innovative neural architectures that integrate character-based and distributional word representations, offering improved model sensitivity to both orthographic features and word context. meanwhile, @xcite propose a domain-specific approach to extracting relational information from scientific texts by fine-tuning gpt-3 models, thereby enabling non-nlp experts to generate structured datasets for specialized tasks. @xcite address large labeled data requirements in ner by combining deep learning with active learning, introducing a cnn-cnn-lstm architecture for incremental training. cui et al. (2023) present the softlexicon-roberta-bilstm-crf model for chinese electronic medical records, while chuang et al. (2023) explore gpt-j for prompt generation in periodontal diagnosis extraction. wu et al. (2021) propose the ra-rc model for chinese clinical ner, combining radical features with deep learning. for legal ner, zhang et al. (2023) introduce a method using roberta and globalpointer for chinese legal documents, fusing character-level and word-level features to identify nested entities. addressing cross-lingual challenges, chan et al. (2023) investigate task learning and data augmentation for ner in low-resource filipino, highlighting transfer learning's importance. alshammari and alanazi (2021) provide a comprehensive study of transformer-based models (bert, albert, xlm-roberta) for ner using the conll dataset, emphasizing preprocessing and fine-tuning. in the realm of active learning, chen et al. (2015) examine strategies for clinical ner, while le et al. (2023) address train-test distribution misalignment using feature matching. lastly, tchoua et al. (2019) explore active learning for ner in scientific texts, developing the polyner system to reduce dependency on large annotated datasets in polymer science. these studies demonstrate the ongoing efforts to enhance ner performance across various domains and languages, often focusing on reducing annotation requirements and improving efficiency in specialized fields. ## results and discussion the results of the model training and evaluation are presented across three sets: training, validation, and test. ## conclusion this paper demonstrates the feasibility and potential of using llms for automated extraction of venue availability information in marc standard format. the roberta-based model showed promising results in identifying and categorizing relevant information from unstructured text, with consistent improvements observed throughout the training process. this research enhances data management by automating the extraction and structuring of venue availability information, improving accessibility through marc standards for better usability across stakeholders. the scalability of the transformer-based roberta model allows for adaptation to larger datasets and other community service types, while also representing an innovative use of advanced nlp techniques to address real-world challenges in community information management. further experimentation with model architectures, training regimes, and hyperparameters could enhance performance, while exploring ensemble methods may improve robustness by leveraging the strengths of different models. additionally, investigating few-shot learning techniques might enable the model to adapt to new types of venue information or regional variations with minimal training. moreover, data enhancement can be achieved through several strategies: employing data augmentation techniques like back-translation or synonym replacement to artificially expand the training dataset may enhance model generalization; increasing experimentation with active learning, where the model identifies informative samples for human annotation, could more efficiently improve the training dataset; and incorporating venue information from various geographic regions could better equip the model to manage regional variations in terminology and venue descriptions.