Productivity and semantic transparency

We used word embeddings to study the relation between productivity and semantic transparency. We compiled a dataset with around 2700 two-syllable compounds that shared position-specific constituents (henceforth pivots) and some 1100 suffixed words. For each pivot and suffix, we calculated measures of productivity as well as measures of semantic transparency. For compounds, productivity (P) was negatively correlated with the number of types (V) and with the semantic similarity between non-pivot constituents and their compounds. Conversely, the greater semantic similarity of the pivot with either the compound or the non-pivot constituent predicted higher degrees of productivity. Visualization with t-SNE revealed clustering of suffixed words’ embeddings, but no by-pivot clustering for compounds, except for a minority of pivots whose regions in semantic space did not contain intruding unrelated compounds. A subset of these pivots was found to realize a fixed shift in semantic space from the base word to the corresponding compound, a property that also emerged for several suffixes. For these pivots, no correlation between P and V was present. Thus, Mandarin compounds appear to realize, at one extreme, motivated but unsystematic concept formation (where other pivots could just as well have been used), and at the other extreme, systematic suffix-like semantics.