Making sense of spoken plurals

Given corpus-based semantic vectors (known as embeddings in computational linguistics and natural language processing) for pairs of base words and corresponding complex words, several methods have been proposed that take as input the semantic vector of the base word, and that produce as output the vector of the complex word. One such method is illustrated in Figure 1. Given the semantic vectors for two pairs of singulars and plurals (table/tables and pen/pens), and given the semantic vector for banana but no semantic vector for its plural, the semantic vector for bananas is obtained by first calculating the vectors that start at a singular and point to the corresponding plural (represented by blue vectors), and average these, resulting in an average shift vector (in red). This shift vector can then be applied to the vector of banana, resulting in the semantic vector for bananas (lower panel). Kisselew et al. (2015) calculated the average shift vector for each of a large set of German derivational affixes, and showed that this results in high-quality estimates of the meanings of derived complex words. Marelli and Baroni (2015) used a method based on matrix multiplication to obtain predicted semantic vectors for derived words, and showed that this method generated quantitative predictors that help explain variance in measures of lexical processing such as reaction times in visual lexical decision. Figure 1 The average of the shift vectors for given singular-plural pairs (table/tables, pen/pens) is used to calculate the semantic vector of the unknown plural vector of banana.