Second-Order Word Embeddingsfrom Nearest Neighbor Topological Features
Table 5: Precision, recall, and F-score on the MSRPC paraphrase recognition dataset, using the concatenation and subtraction combination methods for sentence embeddings. Highest performing settings are marked in bold.
['Setting', 'Concat Pr', 'Concat Rec', 'Concat F1', 'Subtract Pr', 'Subtract Rec', 'Subtract F1']
set on the MSRPC test set, under the concatenation and subtraction sentence combination schema. With concatenated feature vectors, precision varies slightly from the first-order baseline in the various concatenated settings, but decreases by 1% absolute when using second-order embeddings alone. Interestingly, recall behaves inversely: it decreases significantly in the concatenated settings, but increases when using second-order embeddings alone. The impact of second-order information in the subtractive combination scheme is less clear; as with concatenation, precision drops with second-order embeddings alone, but recall is most strongly affected by using multiple samples in the graph induction step.