Building Context-aware Clause Representations for Situation Entity Type Classification
Table 7: Confusion Matrix of the Paragraph-level Model + CRF on the Training Set of MASC+Wiki with 10-Fold Cross-Validation.
['SE Type', 'SE Type', 'Predicted STA', 'Predicted EVE', 'Predicted REP', 'Predicted GENI', 'Predicted GENA', 'Predicted QUES', 'Predicted IMP']
We can see that the four situation entity types, including two eventualities (STATE and EVENT) and two general statives (GENERIC and GENERALIZING), are often mutually confused with each other. To further improve the performance of situation entity type classification, it is important to accurately detect events within a clause (for fixing STATE/EVENT errors) and identify the genericity of main referents (for fixing STATE/GENERIC and GENERIC/GENERALIZING errors), which can be potentially achieved by incorporating linguistic features into neural net models.