Understanding the Developmental Dynamics of Subject Omission: The Role of Processing Limitations in Learning

P. Bloom’s (1990) data on subject omission are often taken as strong support for the view that child language can be explained in terms of full competence coupled with processing limitations in production. This paper examines whether processing limitations in learning may provide a more parsimonious explanation of the data without the need to assume full competence. We extended P. Bloom’s study by using a larger sample (12 children) and measuring subject-omission phenomena in three developmental phases. The results revealed a Verb Phrase-length effect consistent with that reported by P. Bloom. However, contrary to the predictions of the processing limitations account, the proportion of overt subjects that were pronominal increased with developmental phase. The data were simulated with MOSAIC, a computational model that learns to produce progressively longer utterances as a function of training. MOSAIC was able to capture all of the effects reported by P. Bloom through a resource-limited distributional analysis of child-directed speech. Since MOSAIC does not have any built-in linguistic knowledge, these results show that the phenomena identified by P. Bloom do not constitute evidence for underlying competence on the part of the child. They also underline the need to develop more empirically grounded models of the way that processing limitations in learning might influence the language acquisition process

A comparative evaluation of deep and shallow approaches to the automatic detection of common grammatical errors

This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram frequency statistics: we present two versions, one which uses frequency thresholds and one which uses a decision tree trained on the frequencies of the rarest n-grams in the input sentence. We find that the use of a decision tree improves on the basic approach only for the deep parser-based approach. We also show that combining both the shallow and deep decision tree features is effective. Our evaluation is carried out using a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting grammatical errors into well-formed BNC sentences

Resolving ambiguities in the extraction of syntactic categories through chunking.

In recent years, several authors have investigated how co-occurrence statistics in natural language can act as a cue that children may use to extract syntactic categories for the language they are learning. While some authors have reported encouraging results, it is difficult to evaluate the quality of the syntactic categories derived. It is argued in this paper that traditional measures of accuracy are inherently flawed. A valid evaluation metric needs to consider the well-formedness of utterances generated through a production end. This paper attempts to evaluate the quality of the categories derived from cooccurrence statistics through the use of MOSAIC, a computational model of syntax acquisition that has already been used to simulate several phenomena in child language. It will be shown that derived syntactic categories which may appear to be of high quality will quickly give rise to errors which are not typical of child speech. A solution to this problem is suggested in the form of a chunking mechanism which serves to differentiate between alternative grammatical functions of identical word forms. Results are evaluated in terms of the error rates in utterances produced by the system as well as the quantitative fit to the phenomenon of subject omission

On the resolution of ambiguities in the extraction of syntactic categories through chunking

In recent years, several authors have investigated how co-occurrence statistics in natural language can act as a cue that children may use to extract syntactic categories for the language they are learning. While some authors have reported encouraging results, it is difficult to evaluate the quality of the syntactic categories derived. It is argued in this paper that traditional measures of accuracy are inherently flawed. A valid evaluation metric needs to consider the wellformedness of utterances generated through a production end. This paper attempts to evaluate the quality of the categories derived from co-occurrence statistics through the use of MOSAIC, a computational model of syntax acquisition that has already been used to simulate several phenomena in child language. It is shown that derived syntactic categories that may appear to be of high quality quickly give rise to errors that are not typical of child speech. A solution to this problem is suggested in the form of a chunking mechanism that serves to differentiate between alternative grammatical functions of identical word forms. Results are evaluated in terms of the error rates in utterances produced by the system as well as the quantitative fit to the phenomenon of subject omission

Simple principles for a complex output: An experiment in early syntactic development

A set of iterative mechanisms, the Three-Step Algorithm, is proposed to account for the burst in the syntactic capacities of children over age two. These mechanisms are based on the children’s perception, memory, elementary rule-like behavior and cognitive capacities, and do not require any specific innate grammatical capacities. The relevance of the Three-Step Algorithm is tested, using the large Manchester corpus in the CHILDES database. The results show that 80% of the utterances can be exactly reconstructed and that, when incomplete reconstructions are taken into account, 94% of all utterances are reconstructed. The Three-Step Algorithm should be followed by the progressive acquisition of syntactic categories and use of slot-and-frame structures which lead to a greater and more complex linguistic mastery