Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order di...
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The Internet of Things (IoT) is one of the most intriguing technological revolutions of the last few years due to its rapid expansion. The advancement of IoT applications is dependent on standard and real-time communi...
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The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear...
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The medical context for a drug indication provides crucial information on how the drug can be used in practice. However, the extraction of medical context from drug indications remains poorly explored, as most researc...
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In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeli...
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Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency...
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Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, prev...
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The concept of privacy-by-design has gained considerable attention in the wake of legal requirements that software systems should fulfill. The interconnected knowledge on privacy concepts, legal requirements, and soft...
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Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings...
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Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi
Big data (BD) has emerged as a transformative force, offering unprecedented opportunities for organizations to extract valuable insights and drive informed decision-making. This research paper presents a comprehensive...
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