Modeling Language Change in Historical Corpora: The Case of Portuguese
This work addresses the challenge of analyzing language evolution for linguists and historians, but it is incremental as it applies existing methods to a specific corpus.
The paper tackled the problem of modeling language change in historical Portuguese texts by using temporal text classification, achieving 99.8% accuracy in predicting publication dates using word unigram features with a Support Vector Machines classifier.
This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to classify texts with respect to their publication date taking into account lexical variation represented as word n-grams, and morphosyntactic variation represented by part-of-speech (POS) distribution. We report results of 99.8% accuracy using word unigram features with a Support Vector Machines classifier to predict the publication date of documents in time intervals of both one century and half a century. A feature analysis is performed to investigate the most informative features for this task and how they are linked to language change.