Embedding Syntax and Semantics of Prepositions via Tensor Decomposition
Table 6: Accuracy in prepositional attachment disambiguation.
['Classifier Embedding method', 'HPCD (enriching) GloVe', 'LRFR Word2vec', 'OntoLSTM Glove- extended', 'FNN Word2vec', 'FNN GloVe', 'FNN Our method (ALS)', 'FNN Our method (WD)']
Baselines. For comparison, we include the following state-of-the-art approaches in preposition attachment disambiguation. (1) Head-Prep-Child-Dist (HPCD) Model Belinkov et al. : this compositional neural network is used to train task-specific representations of prepositions. (2) Low-Rank Feature Representation (LRFR) Yu et al. : this method incorporates word parts, contexts and labels into a tensor, and uses decomposed vectors as features for disambiguation. (3) Ontology LSTM (OntoLSTM) Dasigi et al. : the vectors are initialized with GloVe, extended by AutoExtend Rothe and Schütze Similar to the experiments in the preposition selection task (see Section 4), we also include baselines which have the same feed-forward network architecture but generate features with vectors trained by word2vec and GloVe. Since the attachment disambiguation is a selection task, accuracy is a natural evaluation metric. Result. This result is notable considering that prior competitive approaches have used significant linguistic resources such as VerbNet and WordNet, whereas we use none. With the same feed-forward neural network as the classifier, our tensor-based approaches (both ALS and WD) achieve better performance than word2vec and GloVe.