Organization of Term Associations through a Combination of Text Classification and Clustering
Date Issued
2007
Date
2007
Author(s)
Chou, Tsung-Pei
DOI
zh-TW
Abstract
Terms, short and meaningful word string which extracted from sentences and articles, can be the basic unit of information and guideline of concept. The organization of terms can help user understand topics and therefore grasp the key point quickly. When the sources of terms and requests of user are varied, conventional methods, clustering and classification, cannot satisfy users. The clustered results are lack of comprehensive explanations and the classification method need much manual work.
In this thesis, we develop an approach to combine the clustering and classification methods on term organization which provide a more comprehensive overview on terms. We use clustering method to extract the main topic and then user can decide the target classes from clustering results. Finally, all terms will be classified to their belonging classes. The clustering and classification methods are iterative to achieve a better performance.
Subjects
術語組織
分群技術
分類技術
Term Organizing
Clustering
Classification
World Wide Web
Type
other
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