Feature2Vec: Distributional semantic modelling of human property knowledge
Table 1: Accuracy scores (percent) for retrieving the correct concept from amongst the top N most similar nearest neighbours. For PLSR, predicted feature vectors for test concepts are compared to actual feature vectors. For Feature2Vec, predicted concept word embeddings are compared to the actual GloVe embedding vectors.
['Model', 'McRae Top 1', 'McRae Top 5', 'McRae Top 10', 'McRae Top 20', 'CSLB Top 1', 'CSLB Top 5', 'CSLB Top 10', 'CSLB Top 20']
We first evaluate how well the baseline PLSR model performs on the feature vector reconstruction task used by \newcitefagarasan2015distributional. In this evaluation, the feature vector for a test concept is predicted and we test whether the real concept vector is within the top N most similar neighbours of the predicted vector. For Feature2Vec, we embed property norm features into the GloVe semantic space, giving a representation of properties in terms of GloVe dimensions. To predict a held-out concept embedding, we build a representation of the concept word by averaging the learned feature embedding vectors for that word using the ground truth information from the property norm dataset. This gives a method to construct embeddings for new words using property knowledge and associated production frequencies (for example, for a held-out word unicorn, its GloVe embedding vector might be predicted from all features of horse, along with the features is-white, has-a-horn, and is-fantastical). However, we note that this approach is different to the PLSR models, so we do not make a direct comparison between PLSR and Feature2vec nearest neighbour results. Nevertheless, the results show that the word embeddings composed from the learned Feature2Vec feature embeddings appear relatively frequently amongst the most similar neighbour words in the pretrained GloVe space, indicating that feature embedding composition approximates the original word embedddings reasonably well.