Adaptor Grammars Research Articles

Language change and language evolution: Cousins, siblings, twins?

AbstractThe relationship between “language change” and “language evolution” has recently become subject to some debate regarding the scope of both concepts. It has been claimed that while the latter used to refer to the language origins in the first place, both terms can now, to a certain extent, be used synonymously. In this paper, I argue that this can partly be explained by parallel developments both in historical linguistics and in the field of language evolution research that have led to a considerable amount of convergence between both fields. Both have adopted usage-based approaches and data-driven methods, which entails similar research questions and similar perspectives on the phenomena under investigation. This has ramifications for current models and theories of language change (or evolution). Two approaches in particular, the concept of complex adaptive systems and construction grammar, have been combined in integrated approaches that seek to explain both language emergence and language change over historical time. I discuss the potential and limitations of this integrated approach, and I argue that there is still some unexplored potential for cross-fertilization.

Schema Theory as a Construction Grammar

Construction grammar, recently described as “the fastest growing linguistic and interdisciplinary approach to language” (Goldberg 2013, 30) has its foundations in the psychology of human categorization and other general cognitive abilities. So does schema theory in music. In the first extended comparison of these research programs, we present six central principles of construction grammar and demonstrate their relevance and applicability to schema-theoretic studies of music: 1) grammatical constructions, 2) surface structure, 3) a network of constructions, 4) cross-linguistic variability and generalization, 5) usage-based knowledge, and 6) exemplar models. Because studies in child development have played such an important role in changing how scholars view language acquisition and grammar formation (Tomasello 2003) we have chosen to illustrate many of the arguments with musical examples drawn from eighteenth- and nineteenth-century conservatories. An orphan at one of the eighteenth-century conservatories in Naples, the prodigy Henri Fissot at the Paris Conservatory in the 1850s, and the young Rachmaninoff at the Moscow Conservatory in the 1890s all learned the art of composition through the age-old practice of child apprenticeship. From the musical utterances made by these apprentices in response to exercises and contests one can infer much about the grammar being acquired. Extensive musical examples suggest that construction grammar can provide a model for how a large repertory of learned patterns of varying dimensions can collectively function as a flexible and adaptive music grammar.

Online Adaptor Grammars with Hybrid Inference

Adaptor grammars are a flexible, powerful formalism for defining nonparametric, unsupervised models of grammar productions. This flexibility comes at the cost of expensive inference. We address the difficulty of inference through an online algorithm which uses a hybrid of Markov chain Monte Carlo and variational inference. We show that this inference strategy improves scalability without sacrificing performance on unsupervised word segmentation and topic modeling tasks.

Exploring the Role of Stress in Bayesian Word Segmentation using Adaptor Grammars

Stress has long been established as a major cue in word segmentation for English infants. We show that enabling a current state-of-the-art Bayesian word segmentation model to take advantage of stress cues noticeably improves its performance. We find that the improvements range from 10 to 4%, depending on both the use of phonotactic cues and, to a lesser extent, the amount of evidence available to the learner. We also find that in particular early on, stress cues are much more useful for our model than phonotactic cues by themselves, consistent with the finding that children do seem to use stress cues before they use phonotactic cues. Finally, we study how the model’s knowledge about stress patterns evolves over time. We not only find that our model correctly acquires the most frequent patterns relatively quickly but also that the Unique Stress Constraint that is at the heart of a previously proposed model does not need to be built in but can be acquired jointly with word segmentation.

Minimally-Supervised Morphological Segmentation using Adaptor Grammars

This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.

Adaptive star grammars and their languages

Motivated by applications that require mechanisms for describing the structure of object-oriented programs, adaptive star grammars are introduced, and their fundamental properties are studied. In adaptive star grammars, rules are actually schemata which, via the cloning of so-called multiple nodes, may adapt to potentially infinitely many contexts when they are applied. This mechanism makes adaptive star grammars more powerful than context-free graph grammars. Nevertheless, they turn out to be restricted enough to share some of the basic characteristics of context-free devices. In particular, the underlying substitution operator enjoys associativity and confluence properties quite similar to those of context-free graph grammars, and the membership problem for adaptive star grammars is decidable.

Adaptive Grammars - State of Art of the Applied Research on Adaptive Automata

This article presents a line of research, in adaptive automata, based in results for context-free adaptive grammars with appearance checking. The grammatical results are described and exemplified and a strategy is proposed in order to obtain analogous results for the case of the adaptive automata. This strategy is then applied successfully to deduce a restricted version, for the case of the adaptative automata, of the general result for context-free adaptive grammars with appearance checking.

Evolving recursive programs by using adaptive grammar based genetic programming

The publisher apologizes for an error that occurred in the above mentioned article. The error appears in the printed version, as well as in the html and pdf version online. Man Leung Wong is the sole author of this article. His affiliation is listed below.

Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming

Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming