Feature2Vec: Distributional semantic modelling of human property knowledge
Table 2: The average percentage of features that each method can predict for a given concept vector.
['Model', 'McRae Train', 'McRae Test', 'CSLB Train', 'CSLB Test']
The evaluation task that we are most interested in is how well the models can predict feature knowledge for concepts, given the distributional semantic vectors. More specifically, for a given concept with K features, we wish to take the top K predicted features according to each method, and record the overlap with the true property norm listing. In this evaluation, we make direct comparisons between all three models (PLSR 50, PLSR 120, & Feature2Vec). For the PLSR models, we predict the feature vector for a given target word using the embedding vector as input and take the top K weighted features. The results demonstrate that Feature2Vec outperforms the PLSR models on property knowledge prediction, for both training and testing datasets.