论文标题
UOB在Semeval-2020任务1：自动识别新颖的单词感官
UoB at SemEval-2020 Task 1: Automatic Identification of Novel Word Senses
论文作者
论文摘要
就像说语言变化的社会格局一样，语言也会发展以适应用户的需求。词汇语义变化分析是语义分析的新兴领域，旨在追踪随着时间的推移含义的变化。本文提出了一种基于贝叶斯单词感应诱导的词汇语义变化检测方法，适合新颖的单词感官识别。该方法用于提交Semeval-2020任务1，这表明了能够执行半eval任务的方法。从15年的Twitter数据中收集的语料库也使用了相同的方法，然后将其结果用于识别可能是s语实例的单词。
Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users. Lexical semantic change analysis is a burgeoning field of semantic analysis which aims to trace changes in the meanings of words over time. This paper presents an approach to lexical semantic change detection based on Bayesian word sense induction suitable for novel word sense identification. This approach is used for a submission to SemEval-2020 Task 1, which shows the approach to be capable of the SemEval task. The same approach is also applied to a corpus gleaned from 15 years of Twitter data, the results of which are then used to identify words which may be instances of slang.
