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Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify a vast amount of unstructured textual data by category of business environmental analysis framework. Generally, it is difficult to produce high quality and massive training data for machine-learning-based system in terms of cost. Semi-supervised learning techniques are used to improve the classification performance. Additionally, the lack of feature problem that traditional classification systems have suffered is resolved by applying semantic features by utilizing word embedding, a new technique in text mining. Findings The proposed methodology can be used for various business environmental analyses and the system is fully automated in both the training and classifying phases. Semi-supervised learning can solve the problems with insufficient training data. The proposed semantic features can be helpful for improving traditional classification systems. Research limitations/implications This paper focuses on classifying sentences that contain the information of business environmental analysis in large amount of documents. However, the proposed methodology has a limitation on the advanced analyses which can directly help managers establish strategies, since it does not summarize the environmental variables that are implied in the classified sentences. Using the advanced summarization and recommendation techniques could extract the environmental variables among the sentences, and they can assist managers to establish effective strategies. Originality/value The feature selection technique developed in this paper has not been used in traditional systems for business and industry, so that the whole process can be fully automated. It also demonstrates practicality so that it can be applied to various business environmental analysis frameworks. In addition, the system is more economical than traditional systems because of semi-supervised learning, and can resolve the lack of feature problem that traditional systems suffer. This work is valuable for analyzing environmental factors and establishing strategies for companies.

Current machine learning (ML) based automated essay scoring (AES) systems have employed various and vast numbers of features, which have been proven to be useful, in improving the performance of the AES. However, the high-dimensional feature space is not properly represented, due to the large volume of features extracted from the limited training data. As a result, this problem gives rise to poor performance and increased training time for the system. In this paper, we experiment and analyze the effects of feature optimization, including normalization, discretization, and feature selection techniques for different ML algorithms, while taking into consideration the size of the feature space and the performance of the AES. Accordingly, we show that the appropriate feature optimization techniques can reduce the dimensions of features, thus, contributing to the efficient training and performance improvement of AES.

To estimate design floods for hydraulic structures, statistical methods has been used in the analysis of rainfall data. However, due to the lack of rainfall data in some regions, it is difficult to apply the statistical methods for estimation of design rainfall. In addition, increased uncertainty of design rainfall arising from the limited rainfall data can become an important factor for determining the design floods. The main objective of this study was to assess the uncertainty of the future design floods under RCP (representative concentration pathways) scenarios using a bootstrap technique. The technique was used in this study to quantify the uncertainty in the estimation of the future design floods. The Yongdang watershed in South Korea, 2,873 ha in size, was selected as the study area. The study results showed that the standard errors of the basin of Yongdang reservoir were calculated as 2.0~6.9 % of probable rainfall. The standard errors of RCP4.5 scenario were higher than the standard errors of RCP8.5 scenario. As the results of estimation of design flood, the ranges of peak flows considered uncertainty were 2.3~7.1 %, and were different each duration and scenario. This study might be expected to be used as one of guidelines to consider when designing hydraulic structures.

In this paper, we propose a maximum entropy-based model, which can mathematically explain the biomolecular event extraction problem. The proposed model generates an event table, which can represent the relationship between an event trigger and its arguments. The complex sentences with distinctive event structures can be also represented by the event table. Previous approaches intuitively designed a pipeline system, which sequentially performs trigger detection and arguments recognition, and thus, did not clearly explain the relationship between identified triggers and arguments. On the other hand, the proposed model generates an event table that can represent triggers, their arguments, and their relationships. The desired events can be easily extracted from the event table. Experimental results show that the proposed model can cover 91.36% of events in the training dataset and that it can achieve a 50.44% recall in the test dataset by using the event table.