Tiina Lindh-Knuutila

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In time series prediction, one does often not know the properties of the underlying system generating the time series. For example, is it a closed system that is generating the time series or are there any external factors influencing the system? As a result of this, you often do not know beforehand whether a time series is stationary or nonstationary, and(More)
In this article, we study the emergence of associations between words and concepts using the self-organizing map. In particular, we explore the meaning negotiations among communicating agents. The self-organizing map is used as a model of an agent's conceptual memory. The concepts are not explicitly given but they are learned by the agent in an unsupervised(More)
In this article, we are studying the differences between the European Union languages using statistical and unsupervised methods. The analysis is conducted in the different levels of language: the lexical, morphological and syntactic. Our premise is that the difficulty of the translation could be perceived as differences or similarities in different levels(More)
Vector space models are used in language processing applications for calculating semantic similarities of words or documents. The vector spaces are generated with feature extraction methods for text data. However, evaluation of the feature extraction methods may be difficult. Indirect evaluation in an application is often time-consuming and the results may(More)
In this article, we test a word vector space model using direct evaluation methods. We show that independent component analysis is able to automatically produce meaningful components that correspond to semantic category labels. We also study the amount of features needed to represent a category using feature selection with syntactic and semantic category(More)
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