Constituent Structure for Filipino: Induction through Probabilistic Approaches

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

An exemplar-based approach to unsupervised parsing

Simon Denni

Disentangling sequential from hierarchical learning in Artificial Grammar Learning: Evidence from a modified Simon Task

In this paper we probe the interaction between sequential and hierarchical learning by investigating implicit learning in a group of school-aged children. We administered a serial reaction time task, in the form of a modified Simon Task in which the stimuli were organised following the rules of two distinct artificial grammars, specifically Lindenmayer systems: the Fibonacci grammar (Fib) and the Skip grammar (a modification of the former). The choice of grammars is determined by the goal of this study, which is to investigate how sensitivity to structure emerges in the course of exposure to an input whose surface transitional properties (by hypothesis) bootstrap structure. The studies conducted to date have been mainly designed to investigate low-level superficial regularities, learnable in purely statistical terms, whereas hierarchical learning has not been effectively investigated yet. The possibility to directly pinpoint the interplay between sequential and hierarchical learning is instead at the core of our study: we presented children with two grammars, Fib and Skip, which share the same transitional regularities, thus providing identical opportunities for sequential learning, while crucially differing in their hierarchical structure. More particularly, there are specific points in the sequence (k-points), which, despite giving rise to the same transitional regularities in the two grammars, support hierarchical reconstruction in Fib but not in Skip. In our protocol, children were simply asked to perform a traditional Simon Task, and they were completely unaware of the real purposes of the task. Results indicate that sequential learning occurred in both grammars, as shown by the decrease in reaction times throughout the task, while differences were found in the sensitivity to k-points: these, we contend, play a role in hierarchical reconstruction in Fib, whereas they are devoid of structural significance in Skip. More particularly, we found that children were faster in correspondence to k-points in sequences produced by Fib, thus providing an entirely new kind of evidence for the hypothesis that implicit learning involves an early activation of strategies of hierarchical reconstruction, based on a straightforward interplay with the statistically-based computation of transitional regularities on the sequences of symbols

Unsupervised syntactic category learning from child-directed speech

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 57-59).The goal of this research was to discover what kinds of syntactic categories can be learned using distributional analysis on linear context of words, specifically in child-directed speech. The idea behind this is that the categories used by children could very well be different from adult categories. There is some evidence that distributional analysis could be used for some aspects of language acquisition, though very strong arguments exist for why it is not enough to acquire grammar. These experiments can help identify what kind of data can be learned from linear context and statistics only. This paper reports the results of three established automatic syntactic category learning algorithms on a small, edited input set of child-directed speech from the CHILDES database. Hierarchical clustering, K-Means analysis, and an implementation of a substitution algorithm are all used to assign syntactic categories to words based on their linear distributional context. Overall, open classes (nouns, verbs, adjectives) were reliably categorized, and some methods were able to distinguish prepositions, adverbs, subjects vs. objects, and verbs by subcategorization frame. The main barrier standing between these methods and human-like categorization is the inability to deal with the ambiguity that is omnipresent in natural language and poses an important problem for future models of syntactic category acquisition.by Olga N. Wichrowska.M.Eng

Distributional phrase structure induction

Unsupervised grammar induction systems commonly judge potential constituents on the basis of their effects on the likelihood of the data. Linguistic justifications of constituency, on the other hand, rely on notions such as substitutability and varying external contexts. We describe two systems for distributional grammar induction which operate on such principles, using part-of-speech tags as the contextual features. The advantages and disadvantages of these systems are examined, including precision/recall trade-offs, error analysis, and extensibility.