Combining Spans into Entities:A Neural Two-Stage Approach for Recognizing Discontiguous Entities
Table 4: Hyperparameters of our joint model.
['word embedding dim', '200']
Since the size of our dataset is relatively small, the dropout is crucial to prevent overfitting considering that the pre-traind word embeddings have the dimension of 200. The length of most segments is not greater than 6, so we set the maximal length c to be 6 to improve the efficiency of segment extraction.