语义变化的特征化综述
Survey in Characterizing Semantic Change
AI总结：
本文综述了语义变化的特征化方法，探讨了语义变化的三种分类：维度变化、方向变化和关系变化，并总结了现有研究的进展与未来方向。
摘要：
活语言不断演变以整合人类社会的文化变化。这种演变通过新词或词义变化表现出来。理解词义对解读不同文化、领域或时期的文本至关重要。在计算机科学中，这些词对翻译、信息检索、问答等计算语言学算法相关。语义变化可能影响这些算法的输出质量。因此，正式理解和特征化这些变化很重要。研究这一影响是一个新兴问题，吸引了计算语言学界的关注。几种方法提出了检测语义变化的高精度方法，但需要更多努力来特征化词义变化并减少其影响。本文提供了现有方法的可理解概述，并正式定义了三种特征化类别：词义更一般或更狭窄（维度变化）、词义更贬义或积极（方向变化）、以及词在比喻或转喻语境中的趋势（关系变化）。我们总结了所选出版物的主要方面，并讨论了语义变化特征化研究的需要和趋势。
英文摘要：
Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or \textbf{semantic changes} of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the \textit{characterization of semantic changes} and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.
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