• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of the Korean Institute of Navigation
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• v.4 no.2
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• pp.45-79
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• 1980

The aim of this paper is to analyze the mechanics of price formation in the tramp shipping. For the purpose of this study, the main characteristics of tramp freight rates and the market is examined, and a brief examination of the nature ofthe costs of operation is given which are essential for the understanding of the functioning of shipping firms as well as for the understanding of developments in the tramp freight market. The demand and supply relationships in the market is also analysed in detail. Tramp shipping is an industry that has a market which functions under conditions that are not dissimilar to the theoretical model of perfect competition. However, it does notmean that tramp shipping market is a perfectly competitive market. It is apparent that this realworld competitive system has its imperfections, which means that the market for tramp shipping is near to being a perfectly competitive market on an internaitonal scale and it is freight are therefore subjext to the laws of supply and demand. In theory, the minimum freight rate in the short term is that at which the lowest cost vessels will lay-up in preference to operating, and is equal to the variable costs minus lay-up costs; and this would imply that in all times except those of full employment for ships there is a tendency for newer low-cost, and, probably, faster vessels to be driving the older high-cost vessels in the breaker's yards. In this case, shipowners may be reluctant to lay-up their ships becasue of obligations to crews, or because they would lose credibility with shippers or financiers, or simply because of lost prestige. Mainly, however, the decision is made on strictly economic grounds. When, for example, the total operating costs minus the likely freight earnings are greater than the cost of taking the ship out of service, maintaining it, and recommissioning it, then a ship may be considered for laying-up; shipowners will, in other words, run the ships at freight earnings below operating costs by as much as the cost of laying them up. As described above, the freight rates fixed on the tramp shipping market are subject to the laws of supply and demand. In other words, the basic properties of supply and demand are of significance so far as price or rate fluctuations in the tramp freight market are concerned. In connection with the same of the demand for tramp shipping services, the following points should be brone in mind: (a) That the magnitude of demand for sea transport of dry cargoes in general and for tramp shipping services in particular is increasing in the long run. (b) That owning to external factors, the demand for tramp shipping services is capable of varying sharphy at a given going of time. (c) The demad for the industry's services tends to be price inelastic in the short run. On the other hand the demand for the services offered by the individual shipping firm tends as a rule to be infinitely price elastic. In the meantime, the properties of the supply of the tramp shipping facilities are that it cannot expand or contract in the short run. Also, that in the long run there is a time-lag between entrepreneurs' decision to expand their fleets and the actual time of delivery of the new vessels. Thus, supply is inelastic and not capable of responding to demand and price changes at a given period of time. In conclusion, it can be safely stated that short-run changes in freight rates are a direct result of variations in the magnitude of demand for tramp shipping facilities, whilest the average level of freight rates is brought down to relatively low levels over prolonged periods of time.