论文标题
qmul-sds @ ducr-ita：评估意大利语中无监督的直觉词法语义分类
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian
论文作者
论文摘要
在本文中，我们介绍了Diacr-Ita 2020任务的系统的结果和主要发现。我们的系统着重于使用训练集和不同语义检测方法的变化。该任务涉及培训，对齐和预测一个单词的向量从两个历时意大利语料库变化。我们证明，与包括逻辑回归和使用精度在内的不同方法相比，使用指南针C-bow模型的暂时词嵌入更有效。我们的模型排名第三，精度为83.3％。
In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task. Our system focuses on using variations of training sets and different semantic detection methods. The task involves training, aligning and predicting a word's vector change from two diachronic Italian corpora. We demonstrate that using Temporal Word Embeddings with a Compass C-BOW model is more effective compared to different approaches including Logistic Regression and a Feed Forward Neural Network using accuracy. Our model ranked 3rd with an accuracy of 83.3%.
