A Morpho-Syntactically Informed LSTM-CRF Modelfor Named Entity Recognition
Table 7: The impact of different components and different component combinations on the performance of our best model.
['[BOLD] Model', '[BOLD] F1']
The basic model we started with is shown on line (4). Then, on lines (1)-(3) we remove different components from this basic model, and on lines (5)-(7) we add POS and morphological information to it. We can see sizeable improvement for the standard Bi-LSTM-CRF model with only word vector representationa and the model with character-level LSTM. Interestingly, there is almost no difference between the suffix and the prefix vectors. We can further see that adding POS11 (5) vector improves the performance by almost four percent absolute. The morphological vectors and POS3 also improved the F1 score to 92.20 points absolute. These improvements show that using known linguistic knowledge such as grammatical features could improve the representation vectors learned over huge text corpora. From the point of view of feature learning, we speculate that vectors trained over texts in morphologically rich languages do not learn enough grammar such as POS and morphology. The character embeddings also seem not to help much. One explanation for this could be that the suffixes and prefixes in Bulgarian are also highly ambiguous.