Detecting Turkish Synonyms Used in Different Time Periods
This addresses the problem of linguistic transformation in Turkish for NLP researchers, but it is incremental as it builds on existing alignment techniques.
The paper tackles the challenge of applying NLP models to historical Turkish texts by proposing two methods for detecting synonyms across different time periods, showing that both outperform a baseline and maintain consistent efficacy from the 1960s to the 1980s, with slight performance decreases in later periods.
Dynamic structure of languages poses significant challenges in applying natural language processing models on historical texts, causing decreased performance in various downstream tasks. Turkish is a prominent example of rapid linguistic transformation due to the language reform in the 20th century. In this paper, we propose two methods for detecting synonyms used in different time periods, focusing on Turkish. In our first method, we use Orthogonal Procrustes method to align the embedding spaces created using documents written in the corresponding time periods. In our second method, we extend the first one by incorporating Spearman's correlation between frequencies of words throughout the years. In our experiments, we show that our proposed methods outperform the baseline method. Furthermore, we observe that the efficacy of our methods remains consistent when the target time period shifts from the 1960s to the 1980s. However, their performance slightly decreases for subsequent time periods.