Words are not Equal: Graded Weighting Model for building Composite Document Vectors
Table 2: Results of Vector Composition with different Operations
['Composition', 'Accuracy']
We, therefore, adopt both simple and idf weighted average methods in our work. The advantage with addition is that, it doesnot increase the dimension of the vector and captures high level semantics with ease. They also use additive composition to reflect semantic dependencies. queen - king ≈ clearly show that vectors of Neural Language Model and Distributed Model when used with additive composition outperform those with multiplicative composition in Paraphrase Classification task. DM vectors outperform by nearly giving accuracy difference of 6%. They also perform very well on Phrase similarity tasks. We, therefore, propose graded weighting schema for better composition of vectors which is described below.