Detecting Syntactic Change Using a Neural Part-of-Speech Tagger
Table 3: Average distance between each time period’s center and the year that minimizes the perplexity value of the corresponding LOWESS curve. For the decade-level metric, the “center” is the middle year of the decade (1815 for 1810s). For the year-level metric, the “center” is the year itself (1803 for 1803).
['[EMPTY]', '[BOLD] Baseline', '[BOLD] Feedforward', '[BOLD] LSTM']
We also confirmed our hypothesis that for both types of models the prediction error for decade buckets would be lower than for year buckets. For all decades, the feedforward year of composition predictions tended to be skewed towards the middle years of the data set (late 1800s and early 1900s). These findings suggest that the representation of syntactic change learned by the LSTM can be leveraged to date new text.