Predicting is not explaining: targeted learning of the dative alternation

Antoine Chambaz (1) , Guillaume Desagulier (2)

1 MODAL'X - Modélisation aléatoire de Paris X
2 MoDyCo - Modèles, Dynamiques, Corpus

We advocate for ambitious corpus linguistics drawing inspiration from the latest developments of semiparametrics for a modern targeted learning. Transgressing discipline-specific borders, we adapt an approach that has proven successful in biostatistics and apply it to the well-travelled case study of the dative alternation in English. The essence of the approach hinges on causal analysis and targeted minimum loss estimation (TMLE). Through causal analysis, we operationalize the set of scientific questions that we wish to address regarding the dative alternation. Drawing on the philosophy of TMLE, we answer these questions by targeting some versatile machine learners. We derive estimates and confidence regions for well-defined parameters that can be interpreted as the influence of each contextual variable on the outcome of the alternation (prepositional vs double-object), all other things being equal.

Mots clés: statistics; linguistics; causal analysis; dative alternation

Mots clés: statistique; linguistique; analyse causale; alternance dative

Domaines: Statistiques [math.ST]; Théorie [stat.TH]; Linguistique

Antoine Chambaz, Guillaume Desagulier. Predicting is not explaining: targeted learning of the dative alternation. 2014. ⟨hal-01073005v1⟩