Adaptive Feature Extractor of Global Representation and Local Semantics for Text Classification
نویسندگان
چکیده
منابع مشابه
Feature Selection and Representation in Text Classification
Text classification remains an important practical application of both modern machine learning (ML) and natural language processing (NLP) techniques. The influence of these disparate areas of research has contributed much to the success of current state of the art classification methods. This essay provides an overview of the field of text classification, and investigates in particular the topi...
متن کاملCluster Based Symbolic Representation and Feature Selection for Text Classification
In this paper, we propose a new method of representing documents based on clustering of term frequency vectors. For each class of documents we propose to create multiple clusters to preserve the intraclass variations. Term frequency vectors of each cluster are used to form a symbolic representation by the use of interval valued features. Subsequently we propose a novel symbolic method for featu...
متن کاملA Joint Semantic Vector Representation Model for Text Clustering and Classification
Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...
متن کاملAn Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification
The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...
متن کاملA global-ranking local feature selection method for text categorization
In this paper, we propose a filtering method for feature selection called ALOFT (At Least One FeaTure). The proposed method focuses on specific characteristics of text categorization domain. Also, it ensures that every document in the training set is represented by at least one feature and the number of selected features is determined in a data-driven way. We compare the effectiveness of the pr...
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: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3036455