Second-Order Word Embeddingsfrom Nearest Neighbor Topological Features
Table 2: Overall results on the CoNLL-03 NER task; highest performing features are marked in bold. node2vecNN refers to second-order embeddings with a single sample; NN3 denotes the 3-sample case. Concatenated refers to concatenating first-order word2vec embeddings with various second-order embeddings. Pr = Precision, Rec = Recall.
['Setting', 'Pr', 'Rec', 'F1']
The second-order embeddings alone perform nearly as well as word2vec embeddings, achieving 1.5% absolute lower precision, and only 0.5% absolute lower recall. Interestingly, using multiple embedding samples to generate the graph increases precision to nearly 87% at the expense of recall (a drop of over 1% absolute compared to the single sample). Concatenating skip-gram and second-order embeddings gives overall performance similar to skip-gram alone, with slightly lower precision and identical recall (when using a single sample in graph induction) or decreased (with multiple samples or smaller neighborhoods). Increasing the neighborhood size to k=25 appears to introduce additional noise in the neighborhood graph, and decreases performance by a point across the board.