关于词汇歧义检测与词义消歧的综述
A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation
AI总结：
本文综述了自然语言处理中的词义消歧技术，涵盖深度学习与知识图谱方法，介绍了词义扩展等前沿技术及其在生物医学等领域的应用，并指出了语料稀缺等挑战与未来发展方向。
摘要：
本文探讨了自然语言处理（NLP）领域内致力于理解和解决语言歧义的技术，强调了多义性和同形异义等语言现象的复杂性及其对计算模型的影响。本文广泛聚焦于词义消歧（WSD），概述了从深度学习技术到利用 WordNet 等词汇资源和知识图谱的多种方法。文章介绍了词义扩展（WSE）和神经肌病方法等前沿方法，通过预测新词义来提高消歧准确率。它考察了生物医学消歧和特定语言优化中的具体应用，并讨论了认知隐喻在话语分析中的重要性。研究指出了该领域持续存在的挑战，如词义标注语料库的稀缺和非正式临床文本的复杂性。最后提出了未来方向，包括使用大型语言模型、视觉 WSD 和多语言 WSD 系统，强调了解决 NLP 中词汇复杂性的持续演进。这一思维视角突出了该领域的进步，以使计算机能更准确地理解语言。
英文摘要：
This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The paper introduces cutting-edge methodologies like word sense extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy by predicting new word senses. It examines specific applications in biomedical disambiguation and language specific optimisation and discusses the significance of cognitive metaphors in discourse analysis. The research identifies persistent challenges in the field, such as the scarcity of sense annotated corpora and the complexity of informal clinical texts. It concludes by suggesting future directions, including using large language models, visual WSD, and multilingual WSD systems, emphasising the ongoing evolution in addressing lexical complexities in NLP. This thinking perspective highlights the advancement in this field to enable computers to understand language more accurately.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
