Iterated learning and grounding: from holistic to compositional languages

This paper presents a new computational model for studying the origins and evolution of compositional languages grounded through the interaction between agents and their environment. The model is based on previous work on adaptive grounding of lexicons and the iterated learning model. Although the model is still in a developmental phase, the first results show that a compositional language can emerge in which the structure reflects regularities present in the population's environment

Animacy in early New Zealand english

The literature suggests that animacy effects in present-day spoken New Zealand English (NZE) differ from animacy effects in other varieties of English. We seek to determine if such differences have a history in earlier NZE writing or not. We revisit two grammatical phenomena — progressives and genitives — that are well known to be sensitive to animacy effects, and we study these phenomena in corpora sampling 19th- and early 20th-century written NZE; for reference purposes, we also study parallel samples of 19th- and early 20th-century British English and American English. We indeed find significant regional differences between early New Zealand writing and the other varieties in terms of the effect that animacy has on the frequency and probabilities of grammatical phenomena

Controlled Natural Language Generation from a Multilingual FrameNet-based Grammar

This paper presents a currently bilingual but potentially multilingual FrameNet-based grammar library implemented in Grammatical Framework. The contribution of this paper is two-fold. First, it offers a methodological approach to automatically generate the grammar based on semantico-syntactic valence patterns extracted from FrameNet-annotated corpora. Second, it provides a proof of concept for two use cases illustrating how the acquired multilingual grammar can be exploited in different CNL applications in the domains of arts and tourism

On the scope of the referential hierarchy in the typology of grammatical relations

In the late seventies, Bernard Comrie was one of the first linguists to explore the effects of the referential hierarchy (RH) on the distribution of grammatical relations (GRs). The referential hierarchy is also known in the literature as the animacy, empathy or indexibability hierarchy and ranks speech act participants (i.e. first and second person) above third persons, animates above inanimates, or more topical referents above less topical referents. Depending on the language, the hierarchy is sometimes extended by analogy to rankings of possessors above possessees, singulars above plurals, or other notions. In his 1981 textbook, Comrie analyzed RH effects as explaining (a) differential case (or adposition) marking of transitive subject ("A") noun phrases in low RH positions (e.g. inanimate or third person) and of object ("P") noun phrases in high RH positions (e.g. animate or first or second person), and (b) hierarchical verb agreement coupled with a direct vs. inverse distinction, as in Algonquian (Comrie 1981: Chapter 6)

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Morphologically Conditioned Changes in Wanka-Quechua

published or submitted for publicationis peer reviewe

Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition