A Study on Conventional Expression of Hangul Ganchal and Email (조선시대 한글 간찰과 이메일의 상투적 표현 고찰)
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- (The)Study of the Eastern Classic
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- no.49
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- pp.431-459
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- 2012
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The purpose of this article is to compare and analyze the conventional expression of Hangul Ganchal in Cheosun Dynasty and Email. Conventional expression is used remarkably in introductions and conclusions. In introduction, it is used for addressing and safety greetings while in conclusion, it is used for closing address and closing words. In Cheosun Dynasty, an envelope of Ganchal only included the details of the receiver because the letter was genuinely delivered by someone who knew the receiver and the sender very well. An envelope of Ganchal is applicable to the screen of the internet which is used for emailing. In an email, we see the name of the sender and the title of the text and once we click the title, we are able to view the text. The difference between the Ganchal and the email was reflected on how the receiver's detail showed on Ganchal and the email show the sender's details. In a case of addressing in a letter while using the conventional expression, we can see how we use "To~" in humble term and " ~께" in a honorific term. We confirmed that the conventional expression has not yet settled in both of the Gnachal and email for the seasonal greetings. The safety greetings comprised with both of the senders' and receivers' latest updates. In Ganchal, this composition is well described conventionally, whereas in emails, only the receivers' latest news are written but the senders' latest updates are hard to be seen throughout the text. In Ganchal's closing section, the closing address and closing words were expressed conventionally. However, in the case of email; those were again hard to be found throughout. To conclude, in Ganchal the conventional expression was developed and placed in 16thcentury(Sun-eon) when there was a focus in our native language. In 17thcentury(Hyeon-eon), it stood still for a sometime and moved on to 19thcentury(Jing-eon) when there was a strong in fluence of Hangul Ganchal, which resulted in regression to the conservative expression. In general, we are able to confirm that the conventional expression is slowly disappearing.
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.