This paper investigates the acquisition of residual verb second (V2) in three corpora consisting of data from Norwegian-English bilinguals (Emma, Emily, and Sunniva) in order to determine to what extent these structures are affected by cross-linguistic influence (CLI) from Norwegian V2. The three girls exhibit three different patterns with regard to the relevant constructions. They are very target-like in their use of auxiliaries in the relevant structures. However, when it comes to do-support, Emily and Sunniva are equally target-like, while Emma (...) mainly produces non-target-like structures. These either involve the omission of do, or non-target-like movement of a lexical verb. Furthermore, Emma also allows verb movement across the subject with both lexical verbs and auxiliaries in topicalized structures, suggesting that she has overgeneralized residual V2 across verb types and clause types. Emily, on the other hand, is very target-like in structures involving residual V2 in English, but also allows auxiliaries and dummy-do to move across the subject in topicalized structures, overgeneralizing residual V2 to apply to non-subject-initial declaratives. Finally, Sunniva, is very precocious and very target-like in all the relevant structures, which may be an indication of acceleration due to CLI from Norwegian V2. We discuss these results with reference to language balance, finding that the measures available to us suggest that the differences between the children cannot straightforwardly be explained by language dominance. Instead, we suggest that these results can be accounted for by ambiguity in the English system, leaving the data open to several possible interpretations when acquired in contact with the consistent V2 system in Norwegian. This has several consequences: (i) the three girls’ parsers interpret the input differently, (ii) differences between the three children are qualitative rather than quantitative, and (iii) there has to be some mechanism that ensures that the children can ‘recover’ from these non-target-like grammars. In this paper, we will focus on the first two issues. (shrink)

This study investigates the child‐directed speech (CDS) of four Russian‐, six German, and six English‐speaking mothers to their 2‐year‐old children. Typologically Russian has considerably less restricted word order than either German or English, with German showing more word‐order variants than English. This could lead to the prediction that the lexical restrictiveness previously found in the initial strings of English CDS by Cameron‐Faulkner, Lieven, and Tomasello (2003) would not be found in Russian or German CDS. However, despite differences between the three (...) corpora that clearly derive from typological differences between the languages, the most significant finding of this study is a high degree of lexical restrictiveness at the beginnings of CDS utterances in all three languages. (shrink)

This paper examines the suggestion that infinitivaltoomission errors in English-speaking children can result from competition between two constructions (Kirjavainen et al., First Language 29: 313–339, 2009a). Kirjavainen et al. suggested that the acquisition of two (or more) constructions (e.g., WANT-X and WANT-to) for verbs takingto-infinitival complement clauses can lead to infinitivaltoomissions, reflecting the relative frequencies of the constructions in the input. In the present study we analysed 13 English children's corpora to determine whether the presence of a variety of utterance (...) types in the immediate discourse context preceding WANT-to-VP (e.g.,I want to eat it) and erroneous *WANT-zero-VP (e.g., *I want__drink it) constructions was associated with infinitivaltoproduction/omission. This was done separately for the children's own and their interlocutors' discourse utterances. The data show that the occurrence of WANT-toand WANT-X constructions in the prior discourse was associated with differing proportions of infinitivaltoprovision in the WANT-to/zero-VP construction. This together with Kirjavainen et al.'s (First Language 29: 313–339, 2009a) data suggests that children are learning at least two constructions for the verb WANT, that competition contributes to infinitivaltoomissions, and that the strength of competing representations is affected by overall input frequencies and the preceding discourse. (shrink)

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous (...) context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences. (shrink)

The development of abstract schemas and productive rules in language is affected by both token and type frequencies. High token frequencies and surface similarities help to discover formal and functional commonalities between utterances and categorize them as instances of the same schema. High type frequencies and diversity help to develop slots in these schemas, which allow the production and comprehension of novel utterances. In the current study we looked at both token and type frequencies in two related constructions in German (...) child-directed speech: simple transitive and complement-clause constructions. Both constructions contain high frequency verbs, which potentially support the development of verb-specific schemas. However, only the frequent verbs in the transitive constructions occur with a variety of subject types, which also supports the development of a slot in the subject position. We then used an elicited production task to compare 4- and 5-year-old German-speaking children's productivity with simple transitive constructions and complement-clause constructions. The children were prompted to change the subjects of high and low frequency simple transitive verbs, such as essen ‘eat’ and naschen ‘nibble’, mental-state complement-taking verbs, such as denken ‘think’ and vermuten ‘presume’, and communication complement-taking verbs, such as sagen ‘say’ and berichten ‘report’. In accordance with earlier findings, children had less difficulty producing new utterances with high frequency transitive verbs than with low frequency transitive verbs. For the other verb classes, however, we found either reverse frequency effects or no frequency effects. For these verb classes, children's productivity can be determined by diversity rather than simple token frequency. We discuss how token frequency interacts with diversity, discourse function, semantic complexity, and syntactic complexity. (shrink)

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase‐structure trees should be assigned to initial sentences, s/he allows (...) (implicitly) for all possible trees and lets linguistic experience decide which is the “best” tree for each sentence. The best tree is obtained by maximizing “structural analogy” between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus‐based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar‐based and rule‐based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item‐based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions. (shrink)