词义分布能否检测词语的语义变化？
Can Word Sense Distribution Detect Semantic Changes of Words?
AI总结：
本研究探索利用词义分布比较不同时间语料库中目标词的义项分布，通过预训练静态义项嵌入和散度度量，在SemEval 2020 Task 1上验证了该方法能准确检测多语言词语的语义变化。
摘要：
语义变化检测（SCD）是各种需要做出时间敏感预测的自然语言处理应用中的重要任务。随着时间的推移，一些词语以新颖的方式被使用来表达新的含义，这些新含义逐渐确立为现有词语的新义项。另一方面，词义消歧（WSD）方法根据词语出现的上下文，将歧义词与义项标识关联起来。鉴于WSD与SCD之间的这种关系，我们探索了通过比较目标词在两个不同时间步收集的语料库中的义项分布，来预测该词语义是否发生变化的可能性。为此，我们使用预训练的静态义项嵌入自动为语料库中目标词的每次出现标注一个义项标识。接下来，我们计算目标词在给定语料库中的义项标识分布。最后，我们使用不同的散度或距离度量来量化目标词在两个给定语料库之间的语义变化。我们在SemEval 2020 Task 1数据集上的实验结果表明，词义分布可以准确地用于预测英语、德语、瑞典语和拉丁语中词语的语义变化。
英文摘要：
Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to express new meanings, and these new meanings establish themselves as novel senses of existing words. On the other hand, Word Sense Disambiguation (WSD) methods associate ambiguous words with sense ids, depending on the context in which they occur. Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora. For this purpose, we use pretrained static sense embeddings to automatically annotate each occurrence of the target word in a corpus with a sense id. Next, we compute the distribution of sense ids of a target word in a given corpus. Finally, we use different divergence or distance measures to quantify the semantic change of the target word across the two given corpora. Our experimental results on SemEval 2020 Task 1 dataset show that word sense distributions can be accurately used to predict semantic changes of words in English, German, Swedish and Latin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
