A Procedural Definition of Multi-word Lexical Units

Multi-word expressions evade a closed definition. Linguists and computational linguists rely on intuition or build lists of MWE types; while practical, that is scientifically and aesthetically unsatisfying. Without presuming to solve a daunting theoretical problem, we propose a decision procedure which steers a lexicographer toward acceptance or rejection of an N-gram as a lexical unit: a decision tree classifies N-grams as MWE or not MWE. It will succeed if it agrees with the native speakers’ judgment. We need a small, linguistically credible set of features, to contend with the multiplicity of adequate trees. Decision tree induction works with a fixed set of annotated classification examples, but the lexical material for MWE recognition is too large to make annotation feasible. We rely on small-scale statistically significant sampling, and on intuition. Of a few decision trees produced by informed trial and error, we select one we consider best in our circumstances. That tree, deployed in a large-scale wordnet construction project, allowed us to gather dependable statistics on its usefulness in lexicographers’ work. Our goal: systematic expansion of a wordnet by tens of thousands of MWEs in a manner as free of personal biases as possible.

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