Language Evolution and Computation Bibliography

On the one hand, numerous hypotheses have been put forward to account for the emergence of language during the last million years of human evolution. On the other hand, a large majority of linguists considers that nothing can be said about past languages before 8,000 or 10,000 years in the past, given our current knowledge on modern languages. A large gap obviously separates such approaches and conceptions, and has to be crossed to provide a better account of the development of our communicative system. To partially bridge the gap between the former domains, we aim at proposing a plausible scenario for the emergence of languages, with an emphasis on the development of linguistic diversity. The present study will address the question of the monogenesis or polygenesis of modern languages, which is often implicitly biased toward the first hypothesis. Probabilistic and computational models, as well as palaeo-demographic data and evolutionary considerations, will constitute the key points of our proposals.

This paper presents a learning-via-compression approach to unsupervised acquisition of word forms with no a priori knowledge. Following the basic ideas in Solomonoff's theory of inductive inference and Rissanen's MDL framework, the learning is formulated as a process of inferring regularities, in the form of string patterns (i.e., words), from a given set of data. A segmentation algorithm is designed to segment each input utterance into a sequence of word candidates giving an optimal sum of description length gain (DLG). The learning model has a lexical refinement module to exploit this algorithm to derive finer-grained word candidates recursively until no more compression effect is available. Experimental results on an infant-directed speech corpus show that this approach reaches a state-of-art performance in terms of precision and recall of both words and word boundaries

This chapter explores the psychological basis of lexical ambiguity. We compare three ways of meaning calculation, including meanings listed in dictionaries, meanings provided by human subjects, and meanings analyzed by a linguistic theory. Two experiments were conducted using both Chinese and English data. The results suggest that while the numbers of meanings obtained by different methods are significantly different from one another, they are also significantly correlated. Different ways of meaning calculation produce distinct numbers of meanings, though on a relative scale, words with more meanings tend to have greater numbers of meanings throughout. Dictionary meanings are to be distinguished from meanings obtained from subjects both in content and in numbers. These results are then discussed with regard to their methodological implications for further research on psycho- semantics and semantic change.

Language is a symbiotic organism. Language is neither an organ, nor is it an instinct. In the past two and a half million years, we have acquired a genetic predisposition to serve as the host for this symbiont. Like any true symbiont, language enhances our reproductive fitness ...

The evolutionary process works by modifying pre-existing mechanisms, which makes continuity likely. A review of the evidence available to date suggests that there are many aspects of language that show evolutionary continuity, though the direct evidence for syntax and grammar is less clear. However, the universal features of grammar in modern human languages appear to be essentially descriptions of aspects of our basic conceptual universe. It is argued that the most parsimonious model of language evolution involves an increase in conceptual/semantic complexity, which in turn drove the acquisition of syntax and grammar. In this model, universal features of grammar are actually simply reflections of our internal conceptual universe, which are manifested culturally in a variety of ways that are consistent with our pre-linguistic cognitive abilities. This explains both why grammatical rules vary so much across languages, as well as the fact that the commonalities appear to be inherently semantic in nature. An understanding of the way in which concepts are instantiated in the brain, combined with a comparative perspective on brain structure/function relationships, suggest a tight relationship between increasing brain size during hominid evolution and increasing conceptual complexity. A simulation using populations of interacting artificial neural-net agents illustrating this hypothesis is described. The association of brain size and conceptual complexity suggests that language has a deep ancestry.