The Role of Context Types and Dimensionality in Learning Word Embeddings
Table 2: NER MUC out-of-domain results for different embeddings with dimensionality = 25.
['Context type', 'F1 x 100']
In this out-of-domain setting, all types of contexts achieve at least five points improvement over the baseline. Presumably, this is because continuous word embedding features are more robust to differences between train and test data, such as the typical vocabulary used. However, a detailed investigation of out-of-domain settings is out of scope for this paper and left for future work.