Use of a Fast Information Extraction Method as a Decision Support Tool

Ad-hoc extraction of information from documents can ensure the transparency of decisions made by an organization. Different Information Extraction methods have been applied to extract information from various domains. Most widely known methods use manually annotated training documents that require high development time. The automated training methods are not scalable to large application domains. We have developed a semi-automated knowledge-engineering method for building the knowledge-base with minimal efforts. Because our method reduces manual processing of the training data, the development process is very fast. We have developed a prototype application to extract information from the project-reports of the American Recovery and Reinvestment Act (ARRA) of 2009. The fast development process of our system, its scalability to large application domains, and its high extraction effectiveness will help the transparency of management decisions by extracting and mining relevant information

Evaluating Best Practices in Green Supply Chain

Many documents containing information about green supply chain are available in various journal, technical and online news reports. With the rising need of improved environmental performance, it is important that companies implement green supply chain. In this research, we built an experimental system to extract information on green supply chain from electronic documents semi-automatically. This information includes various chemical data, green supply chain standards and strategies. We studied the green supply chain practices of three companies and compared them with the general environment standards set by the U.S. Environmental Protection Agency. Collocation analysis is being used for scientific research. In this paper, we used collocation analysis to evaluate the importance of the green supply chain terms that appear in the files of the three companies with those of the standards

Maintaining computer-based information systems using text-based intelligent systems techniques

In order to incorporate up-to-date quantitative and qualitative information, Computer- Based Information Systems (CBIS) must be able to extract data from unstructured, textual formats such as newspapers and magazines. The process of updating information in a CBIS currently requires large amounts of human effort analyzing and converting data from such sources into formats which information systems can work with. This paper suggests some methods by which the data needs of a CBIS can be handled semi-automatically (Employing both computers and humans) using text-based intelligent systems (TBIS) techniques

Enhancing menu-based executive information systems using a natural language-menu guide*

Ufe argue that current Executive Information Systems (BIS), with simple menu-based user interfaces, support an inadequate range of information needs because a menu base which is too large or changes too rapidly becomes prohibitively difficult to use. We consider an alternative approach to increase executives\u27 access to information bases by incorporating a series of component- specific Natural Language Interfaces (NLI) into the various components of the BIS (database, model base, etc.), and adding a Natural Language-Menu Guide (NLMG) to the traditional menu and graphics based executive information systems. The various NLI will allow users to get access to information bases without having to know information system structures or techniques. At the same time, the NLMG will help users to navigate more easily through large and changing menu bases. The larger, changing menu bases rendered usable by the NLMG can, in turn, offer more options, and options of a more timely nature. The use of a series of smaller, interrelated natural language processing systems, rather than one big NLP system, should also take fuller advantage of the limited nature of current NLP technology

Consumer Feedback: Does Rating Reflect Reviewers’ Feelings?

Consumer feedbacks have been widely used for product improvement. These consumer reviews revealcustomer sentiments (e.g., like/dislike, fulfilled/unfulfilled etc.) about products and the degree of sentiments aswell. These reviews are good sources to gauge customer feelings, which are important to make essentialbusiness decisions. In this research, we analyzed textual movie reviews semi-automatically using linguisticanalysis instead of using manual mechanisms. Generally, adjectives in text reviews express reviewers’ feelingsabout a product while adverbs (gradable) explain the degree of these feelings. Using a well-known moviereview database, we analyzed the pattern of adjectives and adverbs that appeared in reviewers’ comments. Wecompared the frequencies of these adjective and adverbial words with the symbolic ratings (A+ to F) of therespective reviews and found strong correlation between the positive/negative terms (adjectives and adverbs)embedded in the text and their corresponding symbolic ratings

Identifying Opportunities in Multilingual Business Environments Using Environmental Scanning and Text Mining Techniques

The identification of opportunities for growth can be made easier if comprehensive information relevant to the business environment is available to managers. Such recognition of business opportunities can also help sustain competitive advantage. Information relevant to business environment is usually written and posted in many languages and can be accessed from many sources. The collection of this information is time consuming and labor intensive and techniques such as environmental scanning that are proposed in previous research can facilitate this information search. In this study, we propose a technique to automatically perform tasks using text mining tools that search, translate, and extract information from online documents. Updated information produced by these tools will be current and accessible by all levels of management and facilitate managerial decision making

Group impacts using four meeting facilitation techniques

Many studies have investigated the effects of various meeting facilitation techniques on groups, but few have directly compared the effects of different electronic techniques on group interaction. In fact, the vast majority of research in the area of electronic meeting support has used only two techniques: verbal brainstorming and electronic individual poolwriting. This paper describes an experiment involving four groups of 35 undergraduate students each using electronic individual poolwriting, electronic gallery writing, verbal brainstorming, and manual individual poolwriting. Results show that groups using the two electronic techniques were more satisfied and productive and experienced less production blocking and evaluation apprehension. Although there were no significant differences in production blocking, evaluation apprehension, and the number of quality ideas generated between the two electronic techniques, groups were more satisfied with and preferred electronic gallery writing over electronic individual poolwriting

Building Discerning Knowledge Bases from Multiple Source Documents, with Novel Fact Filtering

Information extraction systems that remember only novel information (facts that differ semantically from those previously extracted) can be used to build lean knowledge bases fed from multiple, possibly overlapping sources. In previous research by the authors, natural language processing techniques were used to build a system to extract financial facts from international corporate reports of the Wall Street Journal. We will enhance that system to extract the same types of financial facts from a second source of corporate financial reports: Reuters. The improved system will provide more generality through its ability to extract from multiple sources rather than just one. In addition, it will provide novelty filtering of extracted information, admitting only novel facts into the database, while remembering all sources that a redundant fact came from