Let the semantic neighborhoods of a target word w i computed based on textual and visual features be represented as ordered sets (according to similarity) denoted as T i and V i , respectively.
for larger neighborhoods (> 50 neighbors) the fusion-based approaches perform con-sistently better than the baseline.