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Evaluation of Co-occurring Terms in Clinical Documents Using Latent Semantic Indexing / 대한의료정보학회지
Article in En | WPRIM | ID: wpr-106942
Responsible library: WPRO
ABSTRACT
OBJECTIVES: Measurement of similarities between documents is typically influenced by the sparseness of the term-document matrix employed. Latent semantic indexing (LSI) may improve the results of this type of analysis. METHODS: In this study, LSI was utilized in an attempt to reduce the term vector space of clinical documents and newspaper editorials. RESULTS: After applying LSI, document similarities were revealed more clearly in clinical documents than editorials. Clinical documents which can be characterized with co-occurring medical terms, various expressions for the same concepts, abbreviations, and typographical errors showed increased improvement with regards to a correlation between co-occurring terms and document similarities. CONCLUSIONS: Our results showed that LSI can be used effectively to measure similarities in clinical documents. In addition, correlation between the co-occurrence of terms and similarities realized in this study is an important positive feature associated with LSI.
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Full text: 1 Index: WPRIM Main subject: Semantics / Cluster Analysis / Information Storage and Retrieval / Periodical / Abstracting and Indexing Language: En Journal: Healthcare Informatics Research Year: 2011 Type: Article
Full text: 1 Index: WPRIM Main subject: Semantics / Cluster Analysis / Information Storage and Retrieval / Periodical / Abstracting and Indexing Language: En Journal: Healthcare Informatics Research Year: 2011 Type: Article