Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Table 4: Performance of 6 word embeddings on 8 datasets.
['Dataset embedding', 'Senna50 before', 'Senna50 after', 'Skip50 before', 'Skip50 after', 'HLBL50 before', 'HLBL50 after', 'Glove300 before', 'Glove300 after', 'RNNLM640 before', 'RNNLM640 after', 'Dep1000 before', 'Dep1000 after']
All word embeddings are significantly enhanced after the supervised fine tuning. The performance of HLBL50 embedding is similar to the SENNA50 and Skip50 embeddings, although the initial performance of HLBL50 is not satisfactory. The performance of the best word embedding Glove300 in this experiment is also significantly improved in all datasets. These remarkable improvements may demonstrate that the supervised fine tuning framework may transfer the complementary knowledge from the weak embeddings and lexical semantic resources into the strong embeddings.