Learning Word Embeddings with Text from Different Domains
Table 2: Results on entity recognition.
['Method', 'GENIA  [ITALIC] P', 'GENIA  [ITALIC] R', 'GENIA  [ITALIC] F1', 'ACE  [ITALIC] P', 'ACE  [ITALIC] R', 'ACE  [ITALIC] F1']
From the results we can observe that the embeddings learned using our algorithm can lead to improved performance when used in this particular down-stream NLP task. We note that in such a task, many entities consist of domain-specific terms, therefore learning good representations for such words can be crucial. As we have discussed earlier, our regularization method enables our model to differentiate domain-specific words from words which are more general in the learning process. We believe this mechanism can lead to improved learning of representations for both types of words.