A Customizable Text Classifier for Text Mining
نویسندگان
چکیده
منابع مشابه
A Customizable Text Classifier for Text Mining
Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically ...
متن کاملUsing Fuzzy LR Numbers in Bayesian Text Classifier for Classifying Persian Text Documents
Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...
متن کاملClassifier Based Text Mining for Neural Network
Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the netwo...
متن کاملUsing Fuzzy LR Numbers in Bayesian Text Classifier for Classifying Persian Text Documents
Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Data Science Journal
سال: 2007
ISSN: 1683-1470
DOI: 10.2481/dsj.6.s904