TY - JOUR
T1 - NLP-CIC @ DIACR-Ita
T2 - 7th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2020
AU - Angel, Jason
AU - Rodriguez-Diaz, Carlos A.
AU - Gelbukh, Alexander
AU - Jimenez, Sergio
N1 - Publisher Copyright:
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2020
Y1 - 2020
N2 - We present our systems and findings on unsupervised lexical semantic change for the Italian language in the DIACR-Ita shared-task at EVALITA 2020. The task is to determine whether a target word has evolved its meaning with time, only relying on raw-text from two time-specific datasets. We propose two models representing the target words across the periods to predict the changing words using threshold and voting schemes. Our first model solely relies on part-of-speech usage and an ensemble of distance measures. The second model uses word embedding representation to extract the neighbor's relative distances across spaces and propose “the average of absolute differences” to estimate lexical semantic change. Our models achieved competent results, ranking third in the DIACR-Ita competition. Furthermore, we experiment with the k neighbor parameter of our second model to compare the impact of using “the average of absolute differences” versus the cosine distance used in (Hamilton et al., 2016).
AB - We present our systems and findings on unsupervised lexical semantic change for the Italian language in the DIACR-Ita shared-task at EVALITA 2020. The task is to determine whether a target word has evolved its meaning with time, only relying on raw-text from two time-specific datasets. We propose two models representing the target words across the periods to predict the changing words using threshold and voting schemes. Our first model solely relies on part-of-speech usage and an ensemble of distance measures. The second model uses word embedding representation to extract the neighbor's relative distances across spaces and propose “the average of absolute differences” to estimate lexical semantic change. Our models achieved competent results, ranking third in the DIACR-Ita competition. Furthermore, we experiment with the k neighbor parameter of our second model to compare the impact of using “the average of absolute differences” versus the cosine distance used in (Hamilton et al., 2016).
UR - http://www.scopus.com/inward/record.url?scp=85097561951&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85097561951
SN - 1613-0073
VL - 2765
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 17 December 2020
ER -