Vector Embedding of Wikipedia Concepts and Entities
Table 4: Comparing the results in Phrase Similarity dataset for common entries between all approaches. Rho is Spearmans’s correlation.
['Datasets #', 'Datasets Dataset Name', 'Datasets #Pairs', 'Wikipedia Miner Rho', 'HitSim Rho', 'ConVec Rho', 'ConVec (Heuristic) Rho', 'ConVec (Only Anchors) Rho']
We also compared the results with another structural based similarity approach called HitSim The comparable result of our approach to structural based methods is another proof that we could embed the Wikipedia link structure properly. The result of heuristic based approach is slightly better than our base model. This shows that without sacrificing the accuracy, we could increase the coverage. This means that with the proposed heuristic, we have a vector representation of more Wikipedia pages. Results for only anchors version of ConVec This shows it is better to learn Wikipedia’s concepts vector in the context of other words (words that are not anchored) and as a result to have the same vector space for both Concepts and words.