of the Annual Meeting of the Cognitive Science Society Title Learning Meaning without Primitives : Typology Predicts Developmental Patterns

Does the cognitive naturalness of concepts affect the acquisitional path of meaning? In this paper, we explore the use of crosslinguistically elicited data to approximate cognitive naturalness, following Gentner and Bowerman’s (2009) Typological Prevalence Hypothesis. Using the domain of topological spatial relations as a case study, we show how this kind of data allows us to simulate developmental patterns of order of acquisition and overgeneralization in Dutch. This result suggests that the Typological Prevalence Hypothesis can be computationally operationalized and evaluated, that modeling semantic acquisition without hand-coded semantic primitives is possible, and finally, that crosslinguistic data provides a good source of information to do so. The acquisition of meaning How does a child acquire the language-specific ways of conceptualizing the world that are necessary to form the meaning constructs of her language? Does non-linguistic maturation of the conceptual system play a central role, or is it language that is in the driver’s seat? Gentner and Bowerman (2009) (henceforth: GB) argue that both are true to some extent: language plays a role in constructing subspaces of the conceptual space that constitute the meanings of the language a child learns (Bowerman & Choi, 2001), but the conceptual system is not a blank-slate when language learning commences. Some dimensions of categorization come more natural to the child, being perhaps cognitively more basic, whereas others are harder for the child to grasp (Casasola & Cohen, 2002). Bridging these two insights is GB’s Typological Prevalence Hypothesis (TPH):1 All else being equal, within a given domain, the more frequently a given way of categorizing is found in the languages of the world, the more natural it is for human cognizers, hence the easier it will be for children to learn. (Gentner & Bowerman, 2009, 467) In this paper, we present a computational model that acquires the meaning of linguistic expressions using crosslinguistically elicited data. The goals of this endeavor are twofold: first, to operationalize the TPH, and second, to explore a novel method for studying meaning without hand-coded semantic primitives. Using only crosslinguistic distributional patterns, the approach is similar to distributional semantic approaches to meaning, which use the textual distribution of linguistic items in a corpus to approximate their meaning, often from a cognitive perspective (e.g. Mitchell & Lapata, 2010; Louwerse, 2011). Rather than using corpus data from a single Bowerman (1993) first proposed the TPH, but not by that name. See Jakobson (1971) for an analogous idea for phonology. language, we use crosslinguistically elicited data for a fixed set of stimulus situations. Developing methods circumventing the use of manual features is desirable: although we agree with Xu and Kemp (2010) that discovering crosslinguistically valid semantic primitives is indispensable, this approach also has a fundamental methodological problem. The selection of a finite set of discrete primitives is bound to reflect a coder’s bias. As long as we are not sure how the coder’s culturally informed conceptualization influences coding practices, there is no independent ground truth of what the correct primitives are. Using the same set of stimuli for all informants and letting the elicited data speak for itself largely obviates this problem.2 Using crosslinguistic data furthermore provides a novel basis for computational approaches to meaning, in particular the acquisition of form-meaning pairings. Most acquistional modeling studies pair utterances with primitives that are derived from the language itself (Fazly, Alishahi, & Stevenson, 2010), taken from resources like WordNet (Alishahi, Fazly, Koehne, & Crocker, 2012), or hand-coded on the basis of video data (Fleischman & Roy, 2005; Beekhuizen, Fazly, Nematzadeh, & Stevenson, 2013). Given the crosslinguistic variation noted in papers like Brown (1994) and Bowerman and Choi (2001), all of these methods are very prone to reflect the subdivisions of the conceptual space English makes. The learner’s subdivision of the conceptual hypothesis space is thus already fixed and set to the target language in these approaches. In this study, we show how this can be avoided and more language-neutral primitives can be selected. We apply our model to the domain of topological spatial relations and simulate GB’s findings about the acquisition of prepositions in Dutch: namely that the crosslinguistically common grouping constituting the meaning of the prepositions op (‘horizontal, stable support’, e.g. a cup on a table) and in (‘containment’, e.g. an apple in a bowl) are acquired earlier than and overgeneralized to two prepositions reflecting crosslinguistically less common groupings, aan (‘tenuous support by being attached by one or more points’, e.g. a coat on a rack) and om (‘encircling (support)’, e.g. a ring on a finger). We show how the model is able, first, to learn the extensional meaning of Dutch prepositions reasonably well, and, most importantly, that it simulates the order of acquisition as well as the developmental pattern of overgeneralization GB observed. Finally, we show that this effect is not merely due to the frequencies of the prepositions. Any bias is then moved to the construction of a set of stimuli for which linguistic material is elicited (cf. Lucy, 1997), which is less severe, as the categorization of situations is left to the informants.