Learning Topic-Sensitive Word Representations
Table 4: GAP scores on the candidate ranking task on LS-SE07 for different part-of-speech categories.
['Model', 'Dim = 100 n.', 'Dim = 100 v.', 'Dim = 100 adj.', 'Dim = 100 adv.', 'All', 'Dim = 300 n.', 'Dim = 300 v.', 'Dim = 300 adj.', 'Dim = 300 adv.', 'All', 'Dim = 600 n.', 'Dim = 600 v.', 'Dim = 600 adj.', 'Dim = 600 adv.', 'All']
With 100 dimensions our best model (HTLE) yields improvements across all POS tags, with the largest improvements for adverbs and smallest improvements for adjectives. When increasing the dimension size of embeddings the improvements hold up for all POS tags apart from adverbs. It can be inferred that larger dimension sizes capture semantic similarities for adverbs and context words better than other parts-of-speech categories. Additionally, we observe for both evaluation sets that the improvements are preserved when increasing the embedding size.