Text Document Classification by using WordNet Ontology and Neural Network
نویسندگان
چکیده
منابع مشابه
Neural Network Based Document Clustering Using WordNet Ontologies
Three novel text vector representation approaches for neural network based document clustering are proposed. The first is the extended significance vector model (ESVM), the second is the hypernym significance vector model (HSVM) and the last is the hybrid vector space model (HyM). ESVM extracts the relationship between words and their preferred classified labels. HSVM exploits a semantic relati...
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This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms. Six binary classification tasks of varying difficulty are defined, and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation. Rules are also produced with the commonly used bag-of-words represe...
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Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the pro...
متن کاملWordNet-Based Text Document Clustering
Text document clustering can greatly simplify browsing large collections of documents by reorganizing them into a smaller number of manageable clusters. Algorithms to solve this task exist; however, the algorithms are only as good as the data they work on. Problems include ambiguity and synonymy, the former allowing for erroneous groupings and the latter causing similarities between documents t...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2018
ISSN: 0975-8887
DOI: 10.5120/ijca2018918229