Concept-Based Embeddingsfor Natural Language Processing
Table 3.6: Impact of feature subsets on SkipNER [12]
['SkipNER Feature', 'PER', 'LOC', 'ORG', 'MISC', 'All']
-Ch2S2SS5 -Chx2T1 0.4pt0.1pt We also study the impact of each subset of features used to train the SkipNER model. This result may indicate that the feature subsets are complementary to each other, thus supporting the proposed method of integrating NER-specific features for training word embeddings. Also, note that the Words only subset corresponds to the skip-gram model. Location, and Person names are particularly well recognized by using the POS subset, while self-trained features and taxonomic relations seem more effective to discriminate the class ORG.