To Be or Not To Be a Verbal Multiword Expression:A Quest for Discriminating Features
Table 5: Evaluation results in the PST benchmark setting, and with a manually annotated external corpus
['Evaluation on 10% TrainST (seen twice VMWEs) Method', 'Evaluation on 10% TrainST (seen twice VMWEs) P', 'Evaluation on 10% TrainST (seen twice VMWEs) R', 'Evaluation on 10% TrainST (seen twice VMWEs) F']
As shown in Tab. However, this is much higher than F=0.7003 obtained by varIDE PasquerST2018-W18-4932 with a quite similar method, except that, there, only Naive Bayes with no feature selection is used. This highlights the fact that focusing on a small set of relevant features helps the classification. What is more, our F-measure is slightly higher than the best registered F-score (0.8172) by TRAVERSAL Waszczuk-W18-4931, where logistic regression is applied to sequences of nodes in dependency trees. Additionally, the handling of the variant-of-train VMWEs (i.e. those VMWEs in TestST which appear with a different surface form than in TrainST) is better with our method than with TRAVERSAL (F = 0.7317 vs. 0.7123). We also get better seen-in-train F-scores than the second and third best systems, based on neural networks: GBD-NER-resplit GBD-DBLP:conf/acllaw/BorosB18, which only uses the provided PST corpora, and SHOMA shoma-DBLP:journals/corr/abs-1809-03056, which also employs Wikipedia word embeddings.