Detecting Syntactic Change Using a Neural Part-of-Speech Tagger
Table 1: Test accuracies for all architectural variants. The networks differed in whether year information was included as input and whether the hidden layer had LSTM or feedforward connections.
['[EMPTY]', '[BOLD] Feedforward', '[BOLD] LSTM']
While we are not focused on achieving state-of-the-art POS tagging performance, this relatively high test accuracy suggests that the tagger is legitimate. The LSTM without year These results suggest that temporal information slightly aids tagging. The feedforward taggers with and without year As feedforward networks, unlike LSTMs, do not take into account relations between words, it makes sense that their POS tagging performance is much lower. Additionally, these results bolster the idea that year input improves tagging performance.