The differential role of phonological and distributional cues in grammatical categorisation
Introduction
A necessary prerequisite to producing sentences is that the language learner derives a knowledge of the different grammatical categories and the relations between them. Knowing the category of a word is also a precursor to understanding referents in other's speech. Given the importance of this knowledge in language acquisition it is not surprising that so much debate has centred on this issue, particularly over how grammatical category information is attained. At one level, discussions have concerned whether the categories themselves are innate (Pinker, 1984), or can be learned (though it is, of course, agreed that assignment of lexical items to categories is learned). Assuming that grammatical categories can be learned, another level of debate concerns the sources available to the child in order to learn such categories. Explanations have been offered that invoke the importance of semantic (Bowerman, 1973, Macnamara, 1972), phonological (Kelly, 1992), and distributional (Harris, 1951) cues in the learning process. These have been reviewed in detail elsewhere and so we do not consider them at length here (Christiansen et al., 1998, Christiansen and Dale, 2001, Mintz, 2002, Redington and Chater, 1998). Several studies have explored the potential value of using one type of cue, either phonological or distributional, yet the benefits of integrating information between the different types has not been assessed empirically. This paper provides a test of how information is integrated across these different modalities of cues, employing corpus analyses of child-directed speech and an artificial language learning experiment.
Section snippets
Cues for grammatical categorisation
There are numerous studies that have assessed phonological and distributional information in determining the grammatical category of words. We review these in turn.
Combining distributional and phonological cues
Shi et al.'s (1998) analyses may be interpreted as combining phonological, acoustic and distributional cues, in that frequency and utterance position could be considered to be distributional cues. The differences in distributions for each cue were significant in their study, but it remains unclear how much information each source contributed towards correct classification, and what benefits may accrue from combining information between sources. A number of issues remain unresolved by these
Method
Corpus preparation. The corpus was derived from the CHILDES database of child-directed speech. We extracted all the speech by adults from all the English corpora in the database, resulting in 5,436,855 words. We replaced pauses and stops with boundary markers, producing 1,369,574 utterances in the corpus. The average length of an utterance was 3.97, which is in accordance with an assessment of the Bernstein Ratner fragment of the CHILDES corpus (Bernstein Ratner & Rooney, 2001). The CHILDES
Method
Corpus preparation. The same corpus as for Experiment 1 was employed.
Cue derivation. We tested the extent to which extremely local distributional information—bigram statistics provided useful information about grammatical category. We measured the occurrence of the target word appearing after a context word in contrast to Redington et al. (1998) who assessed the two previous and the two following words, or Mintz (2003) who employed one or more preceding and following words. Our analyses,
Experiment 3: combining phonological and distributional cues
Fig. 3 shows the correct classification of nouns and verbs based on the discriminant analyses of phonological or distributional cues entered separately for different frequency groupings. For high-frequency items, distributional information is extremely useful, but drops off dramatically for lower frequency items. For the phonological cues, the opposite pattern is observed: better performance for lower frequency words.4
Experiment 4: artificial language learning of bigrams
We adapted Valian and Coulson's (1988) artificial language such that category words were presented with different frequencies during training. Our hypothesis was that the association with marker-words would be learned more quickly for the high-frequency category words than the low-frequency category words. We also varied the extent to which there was coherence within the two categories of words. All the words within a category either shared several phonological properties, or none. We were also
General discussion
When cues were considered jointly in the discriminant analyses, classification accuracy increased over when single cues were considered. Cues provided additive value in the classification, contributing towards classification of different items. This was especially true when phonological and distributional cues were considered together. We found confirmation for our hypothesis that phonological and distributional information contributed differentially towards categorisation. At points where
Acknowledgements
All three authors were supported by Human Frontiers of Science Program grant RGP0177/2001-B. The second author was also supported by European Commission Project grant number HPRN-CT-1999-00065.
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