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How (and when) do speakers generalise from memorised exemplars of a construction to a productive schema? The present paper presents a novel take on this issue by offering a corpus-based approach to semantic extension processes. Focusing on clusters of German ADJ N expressions involving the heavily polysemous adjective tief ‚deep’, it is shown that type frequency (a commonly used measure of productivity) needs to be relativised to distinct semantic classes within the overall usage spectrum of a given construction in order to predict the occurrence of novel types within a particular region of this spectrum. Some methodological and theoretical implications for usage-based linguistic model building are considered.
Within cognitive linguistics, there is an increasing awareness that the study of linguistic phenomena needs to be grounded in usage. Ideally, research in cognitive linguistics should be based on authentic language use, its results should be replicable, and its claims falsifiable. Consequently, more and more studies now turn to corpora as a source of data. While corpus-based methodologies have increased in sophistication, the use of corpus data is also associated with a number of unresolved problems. The study of cognition through off-line linguistic data is, arguably, indirect, even if such data fulfils desirable qualities such as being natural, representative and plentiful. Several topics in this context stand out as particularly pressing issues. This discussion note addresses (1) converging evidence from corpora and experimentation, (2) whether corpora mirror psychological reality, (3) the theoretical value of corpus linguistic studies of ‘alternations’, (4) the relation of corpus linguistics and grammaticality judgments, and, lastly, (5) the nature of explanations in cognitive corpus linguistics. We do not claim to resolve these issues nor to cover all possible angles; instead, we strongly encourage reactions and further discussion.
In spite of the obvious importance that is accorded to the notion grammatical construction in any approach that sees itself as a construction grammar (CxG), there is as yet no generally accepted definition of the term across different variants of the framework. In particular, there are different assumptions about which additional requirements a given structure has to meet in order to be recognized as a construction besides being a ‘form-meaning pair’. Since the choice of a particular definition will determine the range of both relevant phenomena and concrete observations to be considered in empirical research within the framework, the issue is not just a mere terminological quibble but has important methodological repercussions especially for quantitative research in areas such as corpus linguistics. The present study illustrates some problems in identifying and delimiting such patterns in naturally occurring text and presents arguments for a usage-based interpretation of the term grammatical construction.
We taught a humanoid robot a number of different actions involving a number of different objects (e.g., touching a green object, moving a red object etc.) alongside a number of simplified linguistic labels for these behaviours (e.g., ‘touch-green’, ‘move-red’ etc.). The robot managed to learn the associations between the behaviours and their linguistic labels, and it succeeded in recognising the compositional structure of the behaviours and their associated linguistic descriptions (ACTION/VERB+OBJECT/NOUN). Moreover, it was able to generalise the learned instructions to novel, previously untrained action+object-combinations (e.g., touch-red). This corresponds to the task of learning and decomposing so-called ‘holophrases’ in early child language acquisition.
Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results.
Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.