论文标题
IMS在Semeval-2020任务1：您能走多低？词汇语义变化检测中的维度
IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection
论文作者
论文摘要
我们介绍了用于SEMEVAL-2020任务1的系统结果，该任务1基于基于Skip-gram和负抽样的Skip-gram，利用了常用的词汇语义变化检测模型。我们的系统着重于向量初始化（VI）对齐，将VI与子任务2的当前顶级模型进行比较，并证明如果我们优化VI维度，则可以优于这些模型。我们证明，性能的差异在很大程度上可以归因于特定于模型的噪声来源，并且我们揭示了维数和VI对齐中频率诱导的噪声之间的牢固关系。我们的结果表明，整合矢量空间比对的词汇语义变化模型应更加关注维数参数的作用。
We present the results of our system for SemEval-2020 Task 1 that exploits a commonly used lexical semantic change detection model based on Skip-Gram with Negative Sampling. Our system focuses on Vector Initialization (VI) alignment, compares VI to the currently top-ranking models for Subtask 2 and demonstrates that these can be outperformed if we optimize VI dimensionality. We demonstrate that differences in performance can largely be attributed to model-specific sources of noise, and we reveal a strong relationship between dimensionality and frequency-induced noise in VI alignment. Our results suggest that lexical semantic change models integrating vector space alignment should pay more attention to the role of the dimensionality parameter.
