A data-driven approach to alternations based on protein-protein interactions

Abstract

Syntactic alternations like the dative shift are well researched. But most decisions
which speakers take are more complex than binary choices. Multifactorial lexicogrammatical
approaches and a large inventory of syntactic patterns are needed to
supplement current approaches. We use the term semantic alternation for the many
ways in which a relation between entities, conveying broadly the same meaning, can be
expressed. We use a well-resourced domain, biomedical research texts, for a corpusdriven
approach. As entities we use proteins, and as relations we use interactions between
them, using Text Mining training data. We discuss three approaches: first, manually
designed syntactic patterns, second a corpus-based semi-automatic approach and
third a machine-learning language model. The machine-learning approach learns the
probability that a syntactic configuration expresses a relevant interaction from an annotated
corpus. The inventory of configurations define the envelope of variation and its
multitude of forms.