论文标题
一个准确的模型，用于预测基于伯特的单词相似性中上下文的（分级）效应
An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert
论文作者
论文摘要
近年来，自然语言处理（NLP）已在语义分析中广泛使用。我们的论文主要讨论了一种方法来分析上下文对人类对类似单词的感知的影响，这是Semeval 2020的第三个任务。我们在计算来自Transformer（Bert）的双向编码器代表产生的两个嵌入向量之间的距离时采用了几种方法。我们的团队Will_go赢得了SubTask1芬兰语言曲目的第一名，这是SubTask1英语曲目中的第二名。
Natural Language Processing (NLP) has been widely used in the semantic analysis in recent years. Our paper mainly discusses a methodology to analyze the effect that context has on human perception of similar words, which is the third task of SemEval 2020. We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT). Our team will_go won the 1st place in Finnish language track of subtask1, the second place in English track of subtask1.
