Unsupervised learning of lexical subclasses from phonotactics

Abstract

Languages are constantly borrowing words from one another. Since the donor and recipient languages typically differ in their phonology and phonotactics, the native words and the loanwords of the borrower language can also exhibit dierent phonology/ phonotactics. Accordingly, it has been proposed that the phonotactics of languages such as Japanese is better explained if words are classified into etymologically defined sublexica. However, this sublexical analysis is challenged by a learnability problem: the sublexical membership of words is not directly observable. This study applies a state-of-the-art clustering method (a Dirichlet process mixture model) to a substantial number of Japanese and English words extracted from corpora. It turns out that the predicted clusters largely correspond to the etymologically defined sublexica. Since the clustering method is domain-general and not specialized to sublexicon identication, the results can be taken as statistical evidence for the heterogeneous lexica of the two languages. Moreover, the unsupervised nature of the clustering method demonstrates the learnability of sublexica from naturalistic data. The learned sublexica also replicate linguistic characterizations of actual sublexica proposed in previous literature, such as the biased distribution of (certain substrings of) segments to particular sublexica. In addition, the learned sublexica make informative predictions based on previous experimental studies. These results suggest that the predicted sublexica are linguistically sound. Finally, the predicted sublexica reveal hitherto unnoticed phonotactic properties. These discoveries can be used for further investigation of native speakers' knowledge.