Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
Table 3: Naver Sentiment Movie Corpus results. We report accuracy (Acc.), precision (P), recall (R), and F1-score (F1) following park2018subword. Each reported result is the average of several runs initialized with different random seeds.
['[BOLD] Embeddings', '[BOLD] Model: LSTM  [BOLD] Acc.', '[BOLD] Model: LSTM  [BOLD] P', '[BOLD] Model: LSTM  [BOLD] R', '[BOLD] Model: LSTM  [BOLD] F1']
While not strictly proven, we conjecture one possible reason for the unsatisfying performance is the low reliability of the Hanja tagger we leveraged, which is not guaranteed to work well with spoken language. Specifically, we have manually inspected the tagging results and observed that there are some errors which can lead to performance degradation. This observation points out a limitation of our current approach, that is, the dependence on external taggers, encouraging us to develop an integrated approach, leaving as future work not resorting to the taggers.