Vector Embedding of Wikipedia Concepts and Entities
Table 2: Comparing the results of three different versions of ConVec (trained on Wikipedia 2.1B tokens) with Google Freebase pre-trained vectors over Google 100B tokens news dataset in the Phrase Analogy task. The Accuracy (All), shows the coverage and performance of each approach for answering questions. The accuracy for common questions (Accuracy (Commons)), is for fair comparison of each approach. #phrases shows the number of top frequent words of each approach that are used to calculate the accuracy. #found is the number of questions that all 4 words of them are present in the approach dictionary.
['Embedding Name', '#phrases', 'Accuracy (All) #found', 'Accuracy (All) Accuracy', 'Accuracy (Commons) #found', 'Accuracy (Commons) Accuracy']
The first accuracy is to compare the coverage and performance of each approach over the all questions in the test dataset ( Accuracy All). The second accuracy is to compare the methods over only common questions (Accuracy commons). Only common questions between each method are used to compare the Accuracy for Commons scenario. The result of ConVec Heuristic for common questions, argue that this heuristic does not have a significant impact on the quality of base ConVec model and it just improved the coverage (added more concepts to the list of concept vectors). and consequently, the quality of the training corpus is more important than its size.