Abstract
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.
Abstract (translated)
本文探讨了自然语言处理（NLP）领域中关注理解和解决语言中歧义的技术，重点关注诸如语义多义性和同义词现象等语言现象的复杂性及其对计算模型的影响。文章对词义消歧（WSD）进行了深入探讨，概述了包括深度学习技术在内的多种方法，以及如何利用词汇资源和相关知识图谱（如WordNet）。本文介绍了具有前沿性的方法，如词义扩展（WSE）和神经肌肉方法，通过预测新词义来提高消歧准确性。文章考察了生物医学消歧和语言特定的优化应用，并讨论了语义隐喻在语篇分析中的重要性。研究指出，该领域面临持续的挑战，例如语义注释语料库的匮乏和 informal clinical 文本的复杂性。最后，文章提出了未来的研究方向，包括使用大型语言模型、视觉 WSD 和多语言 WSD 系统，强调 NLP 中解决词汇复杂性的不断进化。这种思考方式突出了该领域的发展，使计算机更准确地理解语言。
URL
https://arxiv.org/abs/2403.16129
