Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Division of labor in vocabulary structure
T2 - insights from corpus analyses
AU - Christiansen, Morten H.
AU - Monaghan, Padraic
PY - 2016/7
Y1 - 2016/7
N2 - Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such big data have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of the sign: the longstanding assumption that the relationship between the sound of a word and its meaning is arbitrary. The results reveal a systematic relationship between the sound of a word and its meaning, which is stronger for early acquired words. Moreover, the analyses further uncover a systematic relationship between words and their lexical categoriesnouns and verbs sound differently from each otheraffecting how we learn new words and use them in sentences. Together, these results point to a division of labor between arbitrariness and systematicity in sound-meaning mappings. We conclude by arguing in favor of including big data analyses into the language scientist's methodological toolbox.Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguisticdatabases has a more complete picture begun to emerge of how language is actually used and what information is available as input to language acquisition. Analyses of such big data' have resulted in reappraisals of key assumptions about the nature of language, including the arbitrariness of the sign which is the focus on this paper.
AB - Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such big data have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of the sign: the longstanding assumption that the relationship between the sound of a word and its meaning is arbitrary. The results reveal a systematic relationship between the sound of a word and its meaning, which is stronger for early acquired words. Moreover, the analyses further uncover a systematic relationship between words and their lexical categoriesnouns and verbs sound differently from each otheraffecting how we learn new words and use them in sentences. Together, these results point to a division of labor between arbitrariness and systematicity in sound-meaning mappings. We conclude by arguing in favor of including big data analyses into the language scientist's methodological toolbox.Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguisticdatabases has a more complete picture begun to emerge of how language is actually used and what information is available as input to language acquisition. Analyses of such big data' have resulted in reappraisals of key assumptions about the nature of language, including the arbitrariness of the sign which is the focus on this paper.
KW - Vocabulary
KW - Corpus analysis
KW - Form-meaning mappings
KW - Arbitrariness
KW - Systematicity
KW - Lexical categories
KW - Sound symbolism
KW - Big data
KW - PHONOTACTIC PROBABILITY
KW - WORDS
KW - MODELS
KW - ENGLISH
KW - CATEGORIZATION
KW - ACQUISITION
KW - SOUND
U2 - 10.1111/tops.12164
DO - 10.1111/tops.12164
M3 - Journal article
VL - 8
SP - 610
EP - 624
JO - Topics in Cognitive Science
JF - Topics in Cognitive Science
SN - 1756-8757
IS - 3
ER -