Deep learning approaches to lexical simplification: A survey

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Information Systems Pub Date : 2024-09-02 DOI:10.1007/s10844-024-00882-9

Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri

引用次数: 0

Abstract

Lexical Simplification (LS) is the task of substituting complex words within a sentence for simpler alternatives while maintaining the sentence’s original meaning. LS is the lexical component of Text Simplification (TS) systems with the aim of improving accessibility to various target populations such as individuals with low literacy or reading disabilities. Prior surveys have been published several years before the introduction of transformers, transformer-based large language models (LLMs), and prompt learning that have drastically changed the field of NLP. The high performance of these models has sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published since 2017 on LS and its sub-tasks focusing on deep learning. Finally, we describe available benchmark datasets for the future development of LS systems.

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词法简化的深度学习方法：调查

词法简化（LS）是指在保持句子原意的前提下，将句子中的复杂词语替换为更简单的替代词语。LS 是文本简化（TS）系统中的词法部分，目的是提高各种目标人群（如识字率低或有阅读障碍的个人）的可访问性。在引入转换器、基于转换器的大型语言模型（LLMs）以及迅速学习之前的几年，已经发表了一些先前的调查报告，这些调查报告极大地改变了 NLP 领域。这些模型的高性能再次激发了人们对语言学习的兴趣。为了反映这些最新进展，我们对 2017 年以来发表的关于 LS 及其子任务的论文进行了全面调查，重点关注深度学习。最后，我们介绍了用于 LS 系统未来发展的可用基准数据集。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Intelligent Information Systems 工程技术-计算机：人工智能

CiteScore

7.20

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.