When Meaning Is Not Enough: Distributional and Semantic Cues to Word Categorization in Child Directed Speech

One of the most important tasks in first language development is assigning words to their grammatical category. The Semantic Bootstrapping Hypothesis postulates that, in order to accomplish this task, children are guided by a neat correspondence between semantic and grammatical categories, since nouns typically refer to objects and verbs to actions. It is this correspondence that guides children’s initial word categorization. Other approaches, on the other hand, suggest that children might make use of distributional cues and word contexts to accomplish the word categorization task. According to such approaches, the Semantic Bootstrapping assumption offers an important limitation, as it might not be true that all the nouns that children hear refer to specific objects or people. In order to explore that, we carried out two studies based on analyses of children’s linguistic input. We analyzed child-directed speech addressed to four children under the age of 2;6, taken from the CHILDES database. The corpora were selected from the Manchester corpus. The corpora from the four selected children contained a total of 10,681 word types and 364,196 word tokens. In our first study, discriminant analyses were performed using semantic cues alone. The results show that many of the nouns found in parents’ speech do not relate to specific objects and that semantic information alone might not be sufficient for successful word categorization. Given that there must be an additional source of information which, alongside with semantics, might assist young learners in word categorization, our second study explores the availability of both distributional and semantic cues in child-directed speech. Our results confirm that this combination might yield better results for word categorization. These results are in line with theories that suggest the need for an integration of multiple cues from different sources in language development.