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Item type:Publication, Access status: without files , Solving analogies on words based on minimal complexity transformationsAnalogies are 4-ary relations of the form “A is to B as C is to D”. When A, B and C are fixed, we call analogical equation the problem of finding the correct D. A direct applicative domain is Natural Language Processing, in which it has been shown successful on word inflections, such as conjugation or declension. If most approaches rely on the axioms of proportional analogy to solve these equations, these axioms are known to have limitations, in particular in the nature of the considered flections. In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity. We propose a rough estimation of complexity for word analogies and an algorithm to find the optimal transformations. We illustrate our method on a large-scale benchmark dataset and compare with state-of-the-art approaches to demonstrate the interest of using complexity to solve analogies on words.Publicationtype: Conference Paper19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Opening the Parallelogram: Considerations on Non-Euclidean AnalogiesAnalogical reasoning is a cognitively fundamental way of reasoning by comparing two pairs of elements. Several computational approaches are proposed to efficiently solve analogies: among them, a large number of practical methods rely on either a parallelogram representation of the analogy or, equivalently, a model of proportional analogy. In this paper, we propose to broaden this view by extending the parallelogram representation to differential manifolds, hence spaces where the notion of vectors does not exist. We show that, in this context, some classical properties of analogies do not hold any longer. We illustrate our considerations with two examples: analogies on a sphere and analogies on probability distribution manifold.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A complexity based approach for solving Hofstadter's analogiesAnalogical reasoning is a central problem both for human cognition and for artificial learning. Many aspects of this problem remain unsolved, though, and analogical reasoning is still a difficult task for machines. In this paper, we consider the problem of analogical reasoning and assume that the relevance of a solution can be measured by the complexity of the analogy. This hypothesis is tested in a basic alphanumeric micro-world. In order to compute complexity, we present specifications for a prototype language used to describe analogies. A few elementary operators for this language are exposed, and their complexity is discussed both from a theoretical and practical point of view. We expose several alternative definitions of relevance in analogical reasoning and show how they are related to complexity.Publicationtype: Conference Paper19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Incremental learning with the minimum description length principleWhereas a large number of machine learning methods focus on offline learning over a single batch of data called training data set, the increasing number of automatically generated data leads to the emergence of new issues that offline learning cannot cope with. Incremental learning designates online learning of a model from streaming data. In non-stationary environments, the process generating these data may change over time, hence the learned concept becomes invalid. Adaptation to this non-stationary nature, called concept drift, is an intensively studied topic and can be reached algorithmically by two opposite approaches: active or passive approaches. We propose a formal framework to deal with concept drift, both in active and passive ways. Our framework is derived from the Minimum Description Length principle and exploits the algorithmic theory of information to quantify the model adaptation. We show that this approach is consistent with state of the art techniques and has a valid probabilistic counterpart. We propose two simple algorithms to use our framework in practice and tested both of them on real and simulated data.Publicationtype: Conference Paper22