Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution
Table 2: NFH Identification corpus summary. The train and dev splits are noisy and the test set are gold annotations.
['[EMPTY]', 'train', 'dev', 'test', 'all']
We improve the NFH identification using machine learning. We create a large but noisy data set by considering all the numbers in the corpus and treating the NFH identified by the rule-based approach as positive (79,678 examples) and all other numbers as negative (104,329 examples). We randomly split the dataset into train and development sets in a 90%, 10% split.