Finding the Answers With Definition Models
Table 1: Dictionary definition results in Hill et al. (2015)
['Test Set Unsup. models', 'Test Set W2V add', 'Seen (500 WN defs) -', 'Seen (500 WN defs) -', 'Seen (500 WN defs) -', 'Unseen (500 WN defs) 923', 'Unseen (500 WN defs) .04/.16', 'Unseen (500 WN defs) 163', 'Concept descriptions (200) 339', 'Concept descriptions (200) .07/.30', 'Concept descriptions (200) 150']
Neural models: Recurrent neural network (RNN) Bag-of-words (BoW) The baseline models are based on Mitchell and Lapata’s The resulting embedding is compared with all embeddings in the vocabulary using cosine similarity to rank the candidates. The top 10 and top 100 performance can not be reported for this model, but it is expected that it would also have performed worse. This discrepancy could possibly stem from one of the following: The use of GloVe embeddings rather than W2V. A difference in hyperparameters such as the learning rate optimiser. Some other difference in the code or datasets that would be impossible to uncover without access to the original implementation .