| Conclusions and future work | Our study reached two important conclusions: first, given the same data as input, an unsupervised probabilistic model can outperform a handcrafted rule-based SCF extractor with a predefined inventory. |
| Methodology | As with tGRs, the closed-class tags can be lexicalized, but there are no corresponding feature sets for param (since they are already built from POS tags) or lim (since there is no similar rule-based approach). |
| Results | Table 4: Task-based evaluation of leXicons acquired with each of the eight feature types, and the state-of-the-art rule-based VALEX lexicon. |