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Distributional semantic models are strongly dependent on the size and the quality of the reference corpora, which embeds the commonsense knowledge necessary to build comprehensive models. While high-quality texts containing large-scale commonsense information are present in English, such as Wikipedia, other languages may lack sufficient textual support to build distributional models. This paper proposes using the combination of a lightweight (sloppy) machine translation model and an English Distributional Semantic Model (DSM) to provide higher quality word vectors for languages other than English. Results show that the lightweight MT model introduces significant improvements when compared to language-specific distributional models. Additionally, the lightweight MT outperforms more complex MT methods for the task of word-pair translation.

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases, it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification using one or more relations between entities/term mentions, extending the traditional semantic relation classification task. The proposed model is different from existing approaches which typically use machine learning models built over lexical and distributional word vector features in that is uses a combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem. The proposed approach outperformed existing baselines with regard to F1-score, Accuracy, Precision and Recall.

Different semantic interpretation tasks such as text entailment and question answering require the classification of semantic relations between terms or entities within text. However, in most cases it is not possible to assign a direct semantic relation between entities/terms. This paper proposes an approach for composite semantic relation classification, extending the traditional semantic relation classification task. Different from existing approaches, which use machine learning models built over lexical and distributional word vector features, the proposed model uses the combination of a large commonsense knowledge base of binary relations, a distributional navigational algorithm and sequence classification to provide a solution for the composite semantic relation classification problem., 12 pages, 3 figures, conference

This paper provides a comparative analysis of the performance of four state-of-the-art distributional semantic models DSMs over 11 languages, contrasting the native language-specific models with the use of machine translation over English-based DSMs. The experimental results show that there is a significant improvement average of 16.7i¾?% for the Spearman correlation by using state-of-the-art machine translation approaches. The results also show that the benefit of using the most informative corpus outweighs the possible errors introduced by the machine translation. For all languages, the combination of machine translation over the Word2Vec English distributional model provided the best results consistently average Spearman correlation of0.68.

Investigation of the chaotic behavior of traffic streams at urban intersections due to signals has involved researchers in endeavoring to predict a smooth traffic flow model for stabilizing traffic congestion and avoid unnecessary delays. In this paper, we study a hybrid adaptive model, based on a combination of coloured Petri nets, fuzzy logic and learning automata, to efficiently control traffic signals. We show that in comparison with results found in the literature, vehicle delay time is significantly reduced using the proposed method.

This paper is intended to present a method to find an optimized route with intelligent devices for vehicles. Because the vehicles routing problem is one of the possible applications in which the demands of the driver are not specified, this proposed method will use learning automata and fuzzy logics in dynamic environment in order to learn user behavior to predict future behavior and propose an optimized route for the user. The results show that the proposed route is very close to the user desired one.

Increasing number of vehicles, as the natural consequence of population growth, has caused a significant bottle-neck in transportation network and consequently major delays at intersections. Hence, in this paper we study a hybrid adaptive model, based on combination of Coloured Petri Nets, Fuzzy Logic and Learning Automata to efficiently control traffic signals. We show that in comparison with the results found in the literature the vehicle delay time is drastically reduced using the proposed method.

Designing user interface has special importance in Virtual Learning. Considerable portion of students' time is spent in attempting to learn system, work with present lists in user interface and so forth. Perhaps virtual education system has several computational powers, but it lacks adequate user interface. Then, using system would be difficult and user may encounter many confuses during the work and would be unsuccessful in reaching their goals. These are results of unfamiliarity with fundamental principles and in user interface design and disusing them in generating virtual learning systems. Thus, in any virtual learning system design, it is necessary to consider fundamental principles and rules of the user interface design in such system, and in this manner, in this research an adequate user interface is suggested for e-learning Environment in which has been evaluated with regard to these fundamental principles and rules. Evaluations have been done on this interface show its desirable ability to attract users' satisfaction.