Learning Word Embeddings with Domain Awareness
Table 2: NER results on GENIA.
['Embeddings', 'P', 'R', 'F1']
Overall the embeddings trained in the target domain show worse performance than those trained in the source domain; all the aggregation baselines perform worse than trained on source domain. Such findings indicate that with limited data, the quality of embeddings trained on the target domain is not guaranteed. Interestingly, we find the retrofitting methods obtain the worst results, which show the limitation of such methods in the cold-start scenario with limited data in the target domain. Compared to other embeddings, both our models significantly improve the recall and F1 score. To investigate, we notice that the model with our embeddings extract more entities without greatly reducing precision, while all other embeddings, on the contrary, suffer from precision drop when introducing target domain information. Consider the entities is usually domain-specific terms, high recall is thus an indicator of how the model is performed with target domain knowledge.