Supervised Fine Tuning for Word Embedding with Integrated Knowledge
Table 5: Performance of GloVe on Sentiment Analysis.
['Classifier Task', 'static Original', 'static Trained', 'non-static Original', 'non-static Trained']
No matter the word vectors are directly used as the features or as an initialization of feature parameters, the fine tuned GloVe word embedding outperform the original one for both datasets. The may reveal that the fine tuned word embeddings have better separation capability than the original embeddings for sentiment analysis tasks.