QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian (short paper)

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\%.

[1]  Jure Leskovec,et al.  Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change , 2016, ACL.

[2]  Erik Velldal,et al.  Diachronic word embeddings and semantic shifts: a survey , 2018, COLING.

[3]  Patrick Pantel,et al.  From Frequency to Meaning: Vector Space Models of Semantics , 2010, J. Artif. Intell. Res..

[4]  P. Schönemann,et al.  A generalized solution of the orthogonal procrustes problem , 1966 .

[5]  Barbara McGillivray,et al.  Exploiting the Web for Semantic Change Detection , 2018, DS.

[6]  Barbara McGillivray,et al.  Mining the UK Web Archive for Semantic Change Detection , 2019, RANLP.

[7]  Barbara McGillivray,et al.  Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings , 2019, EMNLP.

[8]  Barbara McGillivray,et al.  SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection , 2020, SEMEVAL.

[9]  Gemma Boleda,et al.  Short-Term Meaning Shift: A Distributional Exploration , 2018, NAACL.

[10]  Charles L. A. Clarke,et al.  Lexical Comparison Between Wikipedia and Twitter Corpora by Using Word Embeddings , 2015, ACL.

[11]  Maarten Marx,et al.  UvA-DARE (Digital Academic Repository) Words are Malleable: Computing Semantic Shifts in Political and Media Discourse , 2017 .

[12]  Dominik Schlechtweg,et al.  A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains , 2019, ACL.

[13]  Suzanne Stevenson,et al.  Automatically Identifying Changes in the Semantic Orientation of Words , 2010, LREC.

[14]  Lars Borin,et al.  Survey of Computational Approaches to Lexical Semantic Change , 2018, 1811.06278.

[15]  Matteo Palmonari,et al.  Training Temporal Word Embeddings with a Compass , 2019, AAAI.

[16]  V. Lenin,et al.  The United States of America , 2002, Government Statistical Agencies and the Politics of Credibility.

[17]  Mirella Lapata,et al.  A Bayesian Model of Diachronic Meaning Change , 2016, TACL.

[18]  Daphna Weinshall,et al.  Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models , 2017, EMNLP.

[19]  Mario Giulianelli,et al.  Analysing Lexical Semantic Change with Contextualised Word Representations , 2020, ACL.

[20]  Maria Liakata,et al.  Autoencoding Word Representations through Time for Semantic Change Detection , 2020, ArXiv.

[21]  Eyal Sagi,et al.  Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space , 2009 .

[22]  Slav Petrov,et al.  Temporal Analysis of Language through Neural Language Models , 2014, LTCSS@ACL.

[23]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.