HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysis
This work addresses the challenge of historical semantic change analysis for linguists and NLP researchers, but it is incremental as it adapts an existing method to new data.
The authors tackled the problem of diachronic lexical semantic analysis by training HistBERT, a BERT-based model on historical corpus data, and found it outperformed original BERT in word similarity and semantic shift tasks, suggesting contextual embeddings' effectiveness depends on temporal text profiles.
Contextualized word embeddings have demonstrated state-of-the-art performance in various natural language processing tasks including those that concern historical semantic change. However, language models such as BERT was trained primarily on contemporary corpus data. To investigate whether training on historical corpus data improves diachronic semantic analysis, we present a pre-trained BERT-based language model, HistBERT, trained on the balanced Corpus of Historical American English. We examine the effectiveness of our approach by comparing the performance of the original BERT and that of HistBERT, and we report promising results in word similarity and semantic shift analysis. Our work suggests that the effectiveness of contextual embeddings in diachronic semantic analysis is dependent on the temporal profile of the input text and care should be taken in applying this methodology to study historical semantic change.