Predicting the Argumenthood of English Prepositional Phrases
Table 2: Model performances on full-sentence, unobserved label included variant of the original classification task (now ternary classification) on the test set (n=18,764).
['Classification model', 'w2v Acc.', 'w2v  [ITALIC] F1', 'Glove Acc.', 'Glove  [ITALIC] F1', 'fastText Acc.', 'fastText  [ITALIC] F1', 'ELMo Acc.', 'ELMo  [ITALIC] F1']
The meaning of the complement noun phrase (NP) of the preposition can also be a clue to determining argumenthood. We did not include the NP as a part of the input in our main task dataset because the argumenthood labels we are using are type-level and not labels for individual instantiation of the types (token-level). However,the NP meanings, especially thematic roles, do play crucial roles in argumenthood. To address this concern, we propose variants of the main task that give prediction models access to information from the NP by providing full sentence inputs. We did not test the concatenation model since the dimensionality of the vectors would be too large with full sentence inputs. All tested models perform over chance, with ELMo achieving the best performance once again. We observe a similar gain of around 3%p by replacing BoW with BiLSTM as in the main task.