Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge BasesIn this paper, we use the terms ”concept” and ”entity” interchangeably.
Table 4: Results of semantic parsing before and after argument type identification, given as percent accuracy. Using CME to identify argument types resulted in improved accuracy on both datasets.
['Dataset', 'GEO', 'ATIS']
Accuracy is defined as the proportion of the input utterances whose logical form is identical to the gold standard. As we can see, our argument type identification scheme resulted in significant accuracy improvements of ∼10% on both datasets.