Learning Concept Taxonomies from Multi-modal Data
Table 2: Comparisons among different variants of our model, fu2014learning and bansalstructured on two tasks. The ancestor-F1 scores are reported.
['Method', '[ITALIC] h=4', '[ITALIC] h=5', '[ITALIC] h=6', '[ITALIC] h=7']
In the hierarchy completion task, we split each tree into 70% nodes for training and 30% for test, and experiment with different h. We compare the following three systems: (1) Fu2014 ; (2) Ours (L): Our model with only language features enabled (i.e. surface features, parent-child word-word relation feature and siblings word-word relation feature); (3) Ours (LV): We observe that the performance gradually drops when h increases, as more nodes are inserted when the tree grows higher, leading to a more complex and difficult taxonomy to be accurately constructed. Overall, our model outperforms Fu2014 in terms of the F1 score, even without visual features. In the most difficult case with h=7, our model still holds an F1 score of 0.42 (2× of Fu2014), demonstrating the superiority of our model. Hierarchy construction. The hierarchy construction task is much more difficult than hierarchy completion task because we need to build a taxonomy from scratch given only a hyper-root. We compare the following methods: (1) Fu2014, (2) Ours (L), and (3) Ours (LV), as described above; (4) Bansal2014: The model by \newcitebansalstructured retrained using our dataset; (5) Ours (LB): By excluding visual features, but including other language features from \newcitebansalstructured; (6) Ours (LVB): Our full model further enhanced with all semantic features from \newcitebansalstructured; (7) Ours (LVB - E): By excluding word embedding-based language features from Ours (LVB).