Species that are underrepresented in the dataset will receive fewer parameter updates. The resulting representations are likely to be of a lower quality than the representations for the more frequently represented species. This is also a problem within natural language processing (NLP). An NLP solution to this problem is to apply an optional post-processing technique called induction . This involves adjusting the learned representations into a more sensible area of the representation space by using the learned vectors of their most similar species. To achieve this, a quadruple tuple of periodic group table number, row number, electronegativity and oxidation state is used to represent each ionic species. The cosine similarity of this 4-dimensional vector is used to determine the nearest neighbours of the species. From this, the induced representation, û can be define from the original representation u: