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Kang, In-Su
- Journal of the Korean Society for information Management
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- v.29 no.4
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- pp.43-60
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- 2012
Identifying citing sentences from article full-text is a prerequisite for creating a variety of future academic information services such as citation-based automatic summarization, automatic generation of review articles, sentiment analysis of citing statements, information retrieval based on citation contexts, etc. However, finding citing sentences is not easy due to the existence of implicit citing sentences which do not have explicit citation markers. While several methods have been proposed to attack this problem for English, it is difficult to find such automatic methods for Korean academic literature. This article presents a rule-based approach to identifying Korean citing sentences. Experiments show that the proposed method could find 30% of implicit citing sentences in our test data in nearly 70% precision.
https://doi.org/10.3743/KOSIM.2012.29.4.043 인용 PDF KSCI

Kang, In-Su
- Journal of the Korea Society of Computer and Information
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- v.22 no.9
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- pp.17-24
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- 2017
Core citation recognition is to identify influential ones among the prior articles that a scholarly article cite. Previous approaches have employed citing-text occurrence information, textual similarities between citing and cited article, etc. This study proposes a term-based approach to core citation recognition, which exploits the importance of individual terms appearing in in-text citation to calculate influence-strength for each cited article. Term importance is computed using various frequency information such as term frequency(tf) in in-text citation, tf in the citing article, inverse sentence frequency in the citing article, inverse document frequency in a collection of articles. Experiments using a previous test set consisting of computational linguistics articles show that the term-based approach performs comparably with the previous approaches. The proposed technique could be easily extended by employing other term units such as n-grams and phrases, or by using new term-importance formulae.
https://doi.org/10.9708/jksci.2017.22.09.017 인용 PDF KSCI

Kang, In-Su
- Journal of the Korea Society of Computer and Information
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- v.21 no.11
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- pp.85-91
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- 2016
This paper proposes a collective approach to cited text recognition by exploiting a set of citing text from different articles citing the same article. First, the proposed method gathers highly-ranked cited sentences from the cited article using a group of citing text to create a collective information of probable cited sentences. Then, such collective information is used to determine final cited sentences among highly-ranked sentences from similarity-based cited text recognition. Experiments have been conducted on the data set which consists of research articles from a computational linguistics domain. Evaluation results showed that the proposed method could improve the performance of similarity-based baseline approaches.
https://doi.org/10.9708/jksci.2016.21.11.085 인용 PDF KSCI