Multi-Property Multi-Label Documents Metadata Recommendation based on Encoder Embeddings (2024)

Abstract: The task of document classification, particularly multi-label classification, presents a significant challenge due to the complexity of assigning multiple relevant labels to each document. This complexity is further amplified in multi-property multi-label classification tasks, where documents must be categorized across various sets of labels. In this research, we introduce an innovative encoder embedding-driven approach to multi-property multi-label document classification that leverages semantic-text similarity and the reuse of pre-existing annotated data to enhance the efficiency and accuracy of the document annotation process. Our method requires only a single model for text similarity, eliminating the need for multiple property-specific classifiers and thereby reducing computational demands and simplifying deployment. We evaluate our approach through a prototype deployed for daily operations, which demonstrates superior performance over existing classification systems. Our contributions include improved accuracy without additional training, increased efficiency, and demonstrated effectiveness in practical applications. The results of our study indicate the potential of our approach to be applied across various domains requiring multi-property multi-label document classification, offering a scalable and adaptable solution for metadata annotation tasks.

## introduction metadata facilitates navigation through extensive document collections, offering insights into data usage, retrieval, traceability, and reusability. it also refines search processes within large datasets. document classification, also known as document annotation, is crucial for information retrieval applications and involves tagging documents with various metadata. this task is laborious, especially when multiple labels per document are required. the complexity increases with multi-property multilabel classification tasks, where each property may contain multiple labels. recent advances in document classification @xcite using natural language processing have significantly improved efficiency, accuracy, and completeness of metadata. however, these methods typically necessitate the development and training of separate models for each classification property, which is resource-intensive and time-consuming. furthermore, the need for continuous retraining to update these models with new properties and labels presents challenges in scalability and adaptability. in this paper, we introduce an innovative approach for multi-property, multi-label document classification that is driven by encoder embeddings. our method capitalizes on semantic-text similarity, using pre-annotated datasets to streamline the annotation process. it stands out by eliminating the need for additional model training or fine-tuning, which greatly reduces computational requirements and eases deployment. our proposed approach is highly applicable in use-cases where multi-label annotated data already exists which is often a use case in various enterprises. we leverage pre-trained models like bert @xcite to avoid fine-tuning, making our solution more scalable and flexible. our prototype, tested at the european commission for daily operations, has outperformed existing systems, enhancing efficiency and accuracy in document annotation. the success of our approach suggests its applicability in various settings that require sophisticated document classification. the key contributions of our study are as follows: ## related work classifying large collections of documents is time intensive and consuming task. however, recent breakthroughs in nlp and the development of large language models (llms) have greatly improved the efficiency of this process. @xcite proposed a framework for classifying documents according to the eurovoc framework in 22 different languages foot_0 by fine-tuning advanced transformerbased pretrained language models. this method has shown significant improvements in classifica-tion accuracy. nonetheless, it requires individual training for each language, leading to high computational demands and difficulties in scaling, particularly when new descriptors or languages need to be added. @xcite introduced annif, a tool that automates the labor-intensive process of subject indexing for librarians. it uses a combination of existing tools and various nlp algorithms to boost accuracy and versatility for different types of documents. however, its effectiveness might be limited in environments with constantly changing content. @xcite developed a technique for classifying legal documents using a dataset annotated with eurovoc labels, comprising 57,000 texts. they found that self-attention mechanisms and domain-specific embeddings notably improve classification performance. however, this method is computationally expensive, particularly for long documents, due to the inclusion of gru units. chang et al. (2020) created the x-transformer model to address issues in extreme multi-label text classification, which involves dealing with vast output spaces and tackling the problem of label sparsity. their model surpasses traditional models in various benchmarks and achieves top-tier results. however, this model requires considerable gpu resources and has scalability issues when faced with large sets of labels due to memory limitations. @xcite tackled the challenge of classifying long legal documents by breaking them down into smaller sections. they found that this segmentation, along with the use of bilstm networks and simpler architectures, made it easier to process lengthy texts. the effectiveness of this approach depends heavily on the quality of the initial segmentation, as poor segmentation can lead to complications in the model's implementation and fine-tuning, especially if it doesn't correctly reflect the thematic or semantic divisions within the documents. ## background and definitions in this section, we provide detailed definitions of the terms and key concepts used in this paper, including document, context, metadata , semantic text similarity, k-nearest neighbors. ## multi-property multi-label documents classification in this section, we present our method for multiproperty multi-label document classification. figure 1 illustrates the key components of our approach, from document datasets to metadata recommendation. ## conclusion and perspectives in this paper, we present a novel method for multiproperty multi-label document classification that leverages an encoder embedding-driven approach. our technique aims to streamline the document an1 2 3 4 5 6 7 8 9 10 0 notation process by utilizing semantic text similarity and the reuse of annotated data. this approach reduces the complexity associated with deploying multiple models, as it relies on a single model to assess text similarity, which results in enhanced efficiency compared to traditional classification methods. the practical implementation of our prototype within the european commission has yielded promising results. empirical results show that our method surpasses the performance of existing systems, delivering superior accuracy and operational efficiency in practical settings. for future work, we aim to assess additional state-of-the-art embedding models to further refine our approach. we also plan to expand our methodology by incorporating graph-based semantic similarity measures.