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QMUL-SDS at DIACR-ITA 2020: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian

Rabab Alkhalifa, Adam Tsakalidis, Arkaitz Zubiaga, Maria Liakata

CLiC-it. .

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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 setsand 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%.
    title={QMUL-SDS@ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian},
    author={Alkhalifa, Rabab and Tsakalidis, Adam and Zubiaga, Arkaitz and Liakata, Maria},
    booktitle={Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), Online. CEUR. org},