Learning Word Embeddings with Domain Awareness
Table 3: POS tagging results on Twitter.
['Embeddings', 'P', 'R', 'F1']
Owing to the limited target domain data, the overall trend of the results from the source, target and aggregated embeddings is similar to the NER task, while our SG-DI and CBOW-DA also show improvement over all baselines. Such results illustrate the effectiveness of our models in utilizing and combining the source and target domain information. Especially when data target domain is limited, such word-pair information is more robust than others in depicting word-word relations. Moreover, since our SG-DI and CBOW-DA embedding are trained on the source domain corpus, they do not suffer from the data sparsity issue as that in target domain embeddings and those retrofitting methods.