312,834 research outputs found
Evaluating the role of quantitative modeling in language evolution
Models are a flourishing and indispensable area of research in language evolution. Here we highlight critical issues in using and interpreting models, and suggest viable approaches. First, contrasting models can explain the same data and similar modelling techniques can lead to diverging conclusions. This should act as a reminder to use the extreme malleability of modelling parsimoniously when interpreting results. Second, quantitative techniques similar to those used in modelling language evolution have proven themselves inadequate in other disciplines. Cross-disciplinary fertilization is crucial to avoid mistakes which have previously occurred in other areas. Finally, experimental validation is necessary both to sharpen models' hypotheses, and to support their conclusions. Our belief is that models should be interpreted as quantitative demonstrations of logical possibilities, rather than as direct sources of evidence. Only an integration of theoretical principles, quantitative proofs and empirical validation can allow research in the evolution of language to progress
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Synthetic modeling of cultural language evolution
Trabajo presentado al EvolangIX Workshop: "Five Approaches to Language Evolution" celebrado en Kyoto (Japón) el 13 de marzo de 2012.Recently cultural theories of language evolution have gained significant momentum in explaining
natural language. This paper reviews agent-based modeling, one of the key methodologies which is in part responsible for these developments. We discuss the most important challenges for a theory of cultural language evolution and the resulting dominant experimental paradigm. The discussion is framed along examples of experiments conducted within the methodology. We focus, in particular, on spatial language as an example of a complex and cognitively central domain treated in a series of robotic experiments.Funding was provided by Sony CSL Paris, the EU FP6 project ECAgents and the EU FP7 project Alear.Peer reviewe
Modeling Interactions Between Language Evolution and Demography
In this article I provide a review of studies that have modeled interactions between language evolution and demographic processes. The models are classifi ed in terms of three different approaches: analytical modeling, agent-based analytical modeling, and agent-based cognitive modeling. I show that these approaches differ in the complexity of interactions that they can handle and that the agent-based cognitive models allow for the most detailed and realistic simulations. Thus readers are provided with a guideline for selecting which approach to use for a given problem. The analytical models are useful for studying interactions between demography and language evolution in terms of high-level processes; the agent-based analytical models are good for studying such interactions in terms of social dynamics without bothering too much about the cognitive mechanisms of language processing; and the agent-based cognitive models are best suited for the study of the interactions between the complex sociocognitive mechanisms underlying language evolution
Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Dynamic topic modeling facilitates the identification of topical trends over
time in temporal collections of unstructured documents. We introduce a novel
unsupervised neural dynamic topic model named as Recurrent Neural
Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each
time influence the topic discovery in the subsequent time steps. We account for
the temporal ordering of documents by explicitly modeling a joint distribution
of latent topical dependencies over time, using distributional estimators with
temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP
research, we demonstrate that compared to state-of-the art topic models, RNNRSM
shows better generalization, topic interpretation, evolution and trends. We
also introduce a metric (named as SPAN) to quantify the capability of dynamic
topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies (NAACL-HLT 2018
Research accomplished at the Knowledge Based Systems Lab: IDEF3, version 1.0
An overview is presented of the foundations and content of the evolving IDEF3 process flow and object state description capture method. This method is currently in beta test. Ongoing efforts in the formulation of formal semantics models for descriptions captured in the outlined form and in the actual application of this method can be expected to cause an evolution in the method language. A language is described for the representation of process and object state centered system description. IDEF3 is a scenario driven process flow modeling methodology created specifically for these types of descriptive activities
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