Search Results

E-commerce platforms require structured product data in the form of attribute-value pairs to offer features such as faceted product search or attribute-based product comparison. However, vendors often provide unstructured product descriptions, ...

Artificial Intelligence (AI) is a key driver of the Industry 4.0 revolution. In industrial automation systems, data points of assets are represented by globally unique identifiers known as “Tags,” which often contain abbreviated asset and ...

Large Language Models (LLMs) have shown their power in information extraction (IE) leveraging text-to-table task. However, suffering from lost-in-the-middle problem, LLMs struggle to extract all the necessary information within longer context, ...

Information is crucial in today’s context, yet less than 20% of companies utilize their unstructured data due to its complexity. Information Extraction (IE) is vital for effective data use, but current IE models face four major issues. First, they often ...

Scientific discovery entails a detailed understanding and structuring of existing hypotheses—a challenging task due to the variety and complexity of the scientific texts. Despite efforts in domains like bio-medicine and invasion biology, there ...

Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that the main reason lies in the lack of extensive data on IE ...

Nominal compound chain extraction (NCCE) represents an emerging task within the domain of natural language processing, exhibiting significant potential for application in various downstream tasks, including relation extraction and summarization, ...

Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts. Recently, Large Language Models (LLMs) with code-style prompts have demonstrated powerful capabilities in IE tasks. ...

The electronics industry is one of the major regulated industries in the United States that is profoundly impacted by environmental issues. In this study, we use natural language processing (NLP) techniques to analyze reports from major electronics ...

Keywords facilitate rapid comprehension of academic papers for scholars, enhancing research efficiency. As some papers lack author‐assigned keywords, automated keyword extraction becomes crucial. Addressing the limited utilization of external ...

Up to this point, keyword extraction task typically relies solely on textual data. Neglecting visual details and audio features from image and audio modalities leads to deficiencies in information richness and overlooks potential correlations, ...

Due to their remarkable performance, general-purpose multimodal pre-trained language models have gained widespread adoption for Document Understanding tasks. The majority of pre-trained language models rely on serialized text, extracted using ...

Businesses often need to query visually rich documents (VRDs), e.g., purchase receipts, medical records, and insurance forms, among many other forms from multiple vendors, to make informed decisions. As such, several techniques have been proposed to ...

This paper presents a novel approach to information extraction (IE) from visually rich documents (VRD) by employing a directed weighted graph representation to capture relationships among various VRD components. In contrast to conventional methods ...

With the advent of end-to-end models and the remarkable performance of foundation models, the question arises regarding the relevance of preliminary steps, such as layout analysis and optical character recognition (OCR), for information extraction ...

This paper introduces Fetch-A-Set (FAS), a comprehensive benchmark tailored for legislative historical document analysis systems, addressing the challenges of large-scale document retrieval in historical contexts. The benchmark comprises a vast ...

In this paper, we address the challenge of effectively utilizing Large Language Models (LLMs) for Visually Rich Document Understanding (VRDU), a key part of intelligent document processing systems. While LLMs excel in various Natural Language ...

Information Extraction processes in handwritten documents tend to rely on obtaining an automatic transcription and performing Named Entity Recognition (NER) over such transcription. For this reason, in publicly available datasets, the performance ...

Product offers on e-commerce websites often consist of a product title and a textual product description. In order to enable features such as faceted product search or to generate product comparison tables, it is necessary to extract structured ...

We study the problem of few-shot learning in Name Entity Recognition(FS-NER). Specifically, unlike other sequence labeling-based models, that mainly focus on better representations, we leverage logit adjustment technology to alleviate the problem ...