Embedding Syntax and Semantics of Prepositions via Tensor Decomposition
Table 4: Performance on preposition selection.
['Dataset', 'Method', 'Precision', 'Recall', 'F1 score']
Result. We note that the proposed tensor embeddings achieve the best performance among all approaches. In particular, the tensor with weighted decomposition has the highest F1 score on the CoNLL dataset–a 6% improvement over the state-of-the-art. However, the tensor with ALS decomposition performs the best on the SE dataset, achieving a 2% improvement over the state-of-the art. We also note that with the same architecture, tensor embeddings perform much better than word2vec and GloVe embeddings on both the datasets. This validates the representation power of tensor embeddings of prepositions.