论文标题
UWB在Semeval-2020任务1：词汇语义变化检测
UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
论文作者
论文摘要
在本文中，我们描述了检测词汇语义变化的方法，即随着时间的流逝，单词感随时间变化。我们研究了两个语料库中特定单词之间的语义差异，这些词是从不同时间段选择的英语，德语，拉丁语和瑞典语中选择的。我们的方法是为Semeval 2020任务1：\ textIt {无监督的词汇语义变更检测检测。}我们在子任务1：二进制更改检测中排名$ 1^{st} $。我们的方法是完全无监督的，语言是独立的。它包括为每个语料库准备一个语义向量空间；使用规范相关分析和正交转换计算早期和后期空间之间的线性变换；并测量来自早期语料库的目标单词转换的向量与后期语料库中目标单词的向量之间的余弦。
In this paper, we describe our method for the detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked $1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2: ranked change detection. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
