Feature Weight Tuning for Recursive Neural Networks
Table 5: Comparison of Different Coherence models. Reported baseline results are reprinted from [34].
['Model', 'Accuracy']
As can be seen, for long documents, bag-of-words (both unigram and diagram) perform quite well and it is difficult to beat. Standard neural models again do not generate competent results compared with bag of word models in this task. But by incorporating weighted tuning mechanism, we got much better performance, roughly 5% when compared against standard neural models. As can be seen, neural models perform pretty well in this task when compared against existing feature based algorithm. From the reported results, better sentence representations are obtained by incorporating “weighted tuning” properties, pushing the state of art of this task to the accuracy of 0.936.