Abstract not found

Over the past five years, the topic of the origins of language is gaining prominence as one of the big unresolved questions of cognitive science. Artificial Intelligence can make a major contribution to this problem by working out precise, testable models using grounded robotic agents which interact with a real world environment and communicate among themselves or with humans about this environment. A potential side effect op this basic research are new technologies for man-machine interaction based on the negotiation of shared conventions.

Abstract. One of the key properties of (natural) languages is that they are hierarchical. Phrases combine into larger phrases eventually covering complete sentences. The semantics of each phrase combine to form the complex meaning of the whole. A key question in explaining the origins and evolution of language is therefore how such hierarchical structures may originate and spread in a population. This paper reports on breakthrough experiments in which unbounded hierarchical structure can emerge as a side-effect of repair strategies in a population of agents. 1

We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learning structured knowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm represents sentences as paths on a graph whose vertices are words (or parts of words). Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are represented by trees composed of significant patterns and their associated equivalence classes. An input module allows the algorithm to be subjected to a standard test of English as a Second Language (ESL) proficiency. The results

This paper introduces an extension of collocational analysis that takes into account grammatical structure and is specifically geared to investigating the interaction of lexemes and the grammatical constructions associated with them. The method is framed in a construction-based approach to language, i.e. it assumes that grammar consists of signs (form-meaning pairs), and is thus not fundamentally different from the lexicon. The method is applied to linguistic expressions at various levels of abstraction (words, semi-fixed phrases, argument structures, tense, aspect and mood). The method has two main applications: first, to increase the adequacy of grammatical description by providing an objective way of identifying the meaning of a grammatical construction and determining the degree to which particular slots in it prefer or are restricted to a particular set of lexemes; second, to provide data for linguistic theory-building

We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively structured constituent patterns, and by placing strings that have an identical backbone and similar context structure into the same equivalence class. The resulting representations constitute an efficient encoding of linguistic knowledge and support systematic generalization to unseen sentences.

For different reasons, speakers re-use recently used or heard linguistic options whenever they can, a tendency which is referred to as ‘persistence ’ in the present paper. The phenomenon has been largely neglected in extant corpus-based, variationist research, and no standard methodology for dealing with the phenomenon is available. By analyzing three well-known alternations (analytic vs. synthetic comparatives, particle placement, and future marker choice) in several spoken corpora of English, this paper demonstrates that factoring in persistence increases the researcher’s ability to account for linguistic variation. It is also shown that persistence itself is subject to several determinants, such as textual distance between two successive choice contexts in discourse, or turn-taking. In conclusion, I argue that persistence is a factor which deserves empirical attention, and that its existence has consequences for both linguistic theory and practice.

Linguistics must again concentrate on the evolutionary nature of language, so that language models are more realistic with respect to human natural languages and have a greater explanatory force. Multi-agent systems are proposed as a possible route to develop such evolutionary models and an example is given of a concrete experiment in the origins and evolution of word-meaning based on a multi-agent approach.

This paper shows how experiments on artificial language evolution can provide highly relevant results for important debates in linguistic theories. It reports on a series of experiments that investigate how semantic roles can emerge in a population of artificial embodied agents and how these agents can build a network of constructions. The experiment also includes a fully operational implementation of how event-specific participant-roles can be fused with the semantic roles of argument-structure constructions and thus contributes to the linguistic debate on how the syntax-semantics interface is organized. 1.

Open-ended language communication remains an enormous challenge for autonomous robots. This paper argues that the notion of a language strategy is the appropriate vehicle for addressing this challenge. A language strategy packages all the procedures that are necessary for playing a language game. We present a specific example of a language strategy for playing an Action Game in which one robot asks another robot to take on a body posture (such as stand or sit), and show how it effectively allows a population of agents to self-organise a perceptually grounded ontology and a lexicon from scratch, without any human intervention. Next, we show how a new language strategy can arise by