Integrating Lexical Knowledge in Word Embeddings using Sprinkling and Retrofitting
Table 2: Performance of the reachability-based embeddings on similarity datasets. Reported numbers are the Spearman correlation coefficients.
['Lexical Knowledge', 'Hops - k', 'Similarity SimLex999', 'Similarity WS353S', 'Similarity RG65', 'Relatedness WS353R', 'Relatedness TR9856', 'No Distinction WS353', 'No Distinction MEN', 'No Distinction MTurk', 'No Distinction RW']
Reachability Matrix is powerful in capturing semantic information: We proposed a simple sprinkling approach in which a zero-one matrix captures the k-hop reachability information between words in a lexical knowledge graph. In order to see how effectively the reachability matrix captures the lexical knowledge, we performed SVD on the reachability matrix and obtained the word embeddings. Interestingly, we clearly observe that the embeddings obtained from the reachability matrix only (without SPPMI matrix) compete strongly with 300 dimensional pretrained GloVe embeddings on the similarity based datasets. The best performing model gives a Spearman correlation which is 0.19 more than GloVe in Simlex999. Similarly, in RG65 and WS353S, the reachability based embeddings compete well with GloVe. Between the choice of PPDB or WordNet as the lexical knowledge sources, PPDB seems to be more helpful. In general, the performance of reachability-based embeddings increases with increasing the number of hops on the similarity datasets.