Confidence Indexing of Automated Detected Synsets: A Case Study on Contemporary Turkish Dictionary

In this study, a novel confidence indexing algorithm is proposed to minimize human labor in controlling the reliability of automatically extracted synsets from a non-machine-readable monolingual dictionary. Contemporary Turkish Dictionary of Turkish Language Association is used as the monolingual dictionary data. First, the synonym relations are extracted by traditional text processing methods from dictionary definitions and a graph is prepared in Lemma-Sense network architecture. After each synonym relation is labeled by a proper confidence index, synonym pairs with desired confidence indexes are analyzed to detect synsets with a spanning tree-based method. This approach can label synsets with one of three cumulative confidence levels (CL-1, CL-2, and CL-3). According to the confidence levels, synsets are compared with KeNet which is the only open access Turkish Wordnet. Consequently, while most matches with the synsets of KeNet is determined in CL-1 and CL-2 confidence levels, the synsets determined at CL-3 level reveal errors in the dictionary definitions. This novel approach does not find only the reliability of automatically detected synsets, but it can also point out errors of detected synsets from the dictionary.

Forecasting Automobile Sales in Turkey with Artificial Neural Networks

This study aims to reveal significant factors which affect automobile sales and estimate the automobile sales in Turkey by using Artificial Neural Network (ANN), ARIMA, and time series decomposition techniques. The forecasting model includes automobile sales, automobile price, Euro and Dollar exchange rate, employment rate, consumer confidence index, oil prices and industrial production confidence index, the probability of buying an automobile, female employment rate, general economic situation, the expectation of general economic situation, financial status of households, expectation of financial status of households. According to the regression results, changes in Dollar exchange rate, the expectation of financial status of households, seasonally adjusted industrial production index, logarithmic form of automobile sales before-one-month which have a significant effect on automobile sales, are found to be the significant variables. The results show that ANN has a better estimation performance with MAPE=1.18% and RMSE=782 values than ARIMA and time series decomposition techniques.

Brazilian farmer confidence in business performance

The aim of this article is to understand how rural producers in Brazil generate their investment expectations in terms of their costs, productivity, and other macroeconomic variables related to agricultural production. This study contributes to the formulation of more efficient public policies that take into account not only market information, but also the expectations of farmers. Based on data from a survey that calculates the rural producer confidence index in Brazil, non-parametric tests were performed to assess the equality of responses attributed by them in different periods, configured as crop development and harvest start, during 2014 and 2017. The results indicate that the farmer’s behavior is sensitive to changes in the market and his business. Productivity is seen as a long-term expectation. The cost of production and the sale price have generated short and medium-term expectations.

A Biform Game Model with the Shapley Allocation Functions

We define the mixed strategy form of the characteristic function of the biform games and build the Shapley allocation function (SAF) on each mixed strategy profile in the second stage of the biform games. SAF provides a more detailed and accurate picture of the fairness of the strategic contribution and reflects the degree of the players’ further choices of strategies. SAF can guarantee the existence of Nash equilibrium in the first stage of the non-cooperative games. The existence and uniqueness of SAF on each mixed strategy profile overcome the defect that the core may be an empty set and provide a fair allocation method when the core element is not unique. Moreover, SAF can be used as an important reference or substitute for the core with the confidence index.

Development of a Flash Flood Confidence Index from Disaster Reports and Geophysical Susceptibility

The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use.