title: Semantic Coherence Facilitates Distributional Learning.
abstract: Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization.
authors.affiliation: Department of Psychology, Stanford University.
authors.forename: Long
authors.initials: L
authors.lastname: Ouyang
authors.affiliation: Department of Cognitive Science, University of California San Diego.
authors.forename: Lera
authors.initials: L
authors.lastname: Boroditsky
authors.affiliation: Department of Psychology, Stanford University.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Frank
mesh_terms: D003463:Cues; D006801:Humans; D007802:Language; D007804:Language Development; D007858:Learning; D014706:Verbal Learning; D014825:Vocabulary
keywords: Distributional learning; Semantic coherence; Word learning