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Word-vector representations associate a high dimensional real-vector to everyword from a corpus. Recently, neural-network based methods have been proposedfor learning this representation from large corpora. This type ofword-to-vector embedding is able to keep, in the learned vector space, some ofthe syntactic and semantic relationships present in the original word corpus.This, in turn, serves to address different types of language classificationtasks by doing algebraic operations defined on the vectors. The generalpractice is to assume that the semantic relationships between the words can beinferred by the application of a-priori specified algebraic operations. Ourgeneral goal in this paper is to show that it is possible to learn methods forword composition in semantic spaces. Instead of expressing the compositionalmethod as an algebraic operation, we will encode it as a program, which can belinear, nonlinear, or involve more intricate expressions. More remarkably, thisprogram will be evolved from a set of initial random programs by means ofgenetic programming (GP). We show that our method is able to reproduce the samebehavior as human-designed algebraic operators. Using a word analogy task asbenchmark, we also show that GP-generated programs are able to obtain accuracyvalues above those produced by the commonly used human-designed rule foralgebraic manipulation of word vectors. Finally, we show the robustness of ourapproach by executing the evolved programs on the word2vec GoogleNews vectors,learned over 3 billion running words, and assessing their accuracy in the sameword analogy task.

Reproducing and learning new algebraic operations on word embeddings using genetic programming