QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian
Queen Mary University of London（伦敦玛丽女王大学）；Imam Abdulrahman bin Faisal University（伊玛目阿卜杜勒拉赫曼·本·费萨尔大学）；Alan Turing Institute（艾伦·图灵研究所）
英文摘要：
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%.
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。
