Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP-related tasks. In the end, this survey briefly discusses the commonly used ML- and DL-based classifiers, evaluation metrics, and the applications of these word embeddings in different NLP tasks.

中文翻译：

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单词表示模型的综合调查：从经典到最先进的单词表示语言模型

词表示一直是自然语言处理（NLP）历史上的一个重要研究领域。了解如此复杂的文本数据势在必行，因为它包含丰富的信息并且可以在各种应用程序中广泛使用。在本次调查中，我们探索了不同的词表示模型及其表达能力，从经典到现代最先进的词表示语言模型 (LMS)。我们描述了各种文本表示方法，模型设计在 NLP 的背景下蓬勃发展，包括 SOTA LMs。这些模型可以将大量文本转换为捕获相同语义信息的有效向量表示。此外，这些表示可以被各种机器学习 (ML) 算法用于各种与 NLP 相关的任务。到底，