[go: up one dir, main page]

Peng et al., 2014 - Google Patents

PU text classification enhanced by term frequency–inverse document frequency‐improved weighting

Peng et al., 2014

Document ID
18141893391316333065
Author
Peng T
Liu L
Zuo W
Publication year
Publication venue
Concurrency and computation: practice and experience

External Links

Snippet

Term frequency–inverse document frequency (TF–IDF), one of the most popular feature (also called term or word) weighting methods used to describe documents in the vector space model and the applications related to text mining and information retrieval, can …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/30707—Clustering or classification into predefined classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30613—Indexing
    • G06F17/30619—Indexing indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G06F17/30595—Relational databases
    • G06F17/30598—Clustering or classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00—Data processing: database and file management or data structures
    • Y10S707/99931—Database or file accessing

Similar Documents

Publication Publication Date Title
Peng et al. PU text classification enhanced by term frequency–inverse document frequency‐improved weighting
Alsmadi et al. Term weighting scheme for short-text classification: Twitter corpuses
Moldagulova et al. Using KNN algorithm for classification of textual documents
CN107992633B (en) Method and system for automatic classification of electronic documents based on keyword features
Petkova et al. Hierarchical language models for expert finding in enterprise corpora
Kalaivani et al. Sentiment classification of movie reviews by supervised machine learning approaches
Basavaraju et al. A novel method of spam mail detection using text based clustering approach
US20090006391A1 (en) Automatic categorization of document through tagging
Duwairi et al. Feature reduction techniques for Arabic text categorization
Hsiao et al. An incremental cluster-based approach to spam filtering
Asim et al. Comparison of feature selection methods in text classification on highly skewed datasets
Alaa A comparative study on Arabic text classification
Khan et al. Lifelong aspect extraction from big data: knowledge engineering
Rahman et al. Text classification using the concept of association rule of data mining
Dommati et al. Bug Classification: Feature Extraction and Comparison of Event Model using Na\" ive Bayes Approach
Murthy et al. A comparative study on term weighting methods for automated telugu text categorization with effective classifiers
Isabella et al. Analysis and evaluation of Feature selectors in opinion mining
Badawi et al. Termset weighting by adapting term weighting schemes to utilize cardinality statistics for binary text categorization
Lahiri et al. Learning from litigation: Graphs and llms for retrieval and reasoning in ediscovery
Govindarajan A novel framework for evaluating the software project management efficiency–an artificial intelligence approach
Kamruzzaman et al. Text categorization using association rule and naive Bayes classifier
Ado et al. Comparative analysis of integrating multiple filter-based feature selection methods using vector magnitude score on text classification
Rosen E-mail Classification in the Haystack Framework
Lee et al. A comparative study on statistical machine learning algorithms and thresholding strategies for automatic text categorization
Decherchi et al. K-means clustering for content-based document management in intelligence