Sri Lalitha et al., 2014 - Google Patents
Semantic Framework to Text Clustering with NeighborsSri Lalitha et al., 2014
- Document ID
- 8894053271983119500
- Author
- Sri Lalitha Y
- Govardhan A
- Publication year
- Publication venue
- ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol II: Hosted by CSI Vishakapatnam Chapter
External Links
Snippet
Conventional document clustering techniques use bag-of-words to represent documents, an often unsatisfactory representation, as it ignores the relationships between words that do not co-occur literally. Including semantic knowledge in text representation we can establish the …
- 238000000034 method 0 abstract description 11
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
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
-
- 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
- G06F17/3061—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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- 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
-
- 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
-
- 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
-
- 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
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99936—Pattern matching access
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Janani et al. | Text document clustering using spectral clustering algorithm with particle swarm optimization | |
Ni et al. | Short text clustering by finding core terms | |
Huang et al. | Text clustering with extended user feedback | |
Alguliev et al. | DESAMC+ DocSum: Differential evolution with self-adaptive mutation and crossover parameters for multi-document summarization | |
Mothe et al. | Automatic keyphrase extraction using graph-based methods | |
Alzuhair et al. | An approach for combining multiple weighting schemes and ranking methods in graph-based multi-document summarization | |
Liu et al. | Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans | |
Bounabi et al. | A comparison of text classification methods using different stemming techniques | |
Cozzolino et al. | Document clustering | |
Sandhya et al. | Analysis of similarity measures with wordnet based text document clustering | |
Jayabharathy et al. | Correlated concept based dynamic document clustering algorithms for newsgroups and scientific literature | |
Lee et al. | A hierarchical document clustering approach with frequent itemsets | |
Goz et al. | SkyWords: An automatic keyword extraction system based on the skyline operator and semantic similarity | |
Pessiot et al. | Improving document clustering in a learned concept space | |
Bounabi et al. | A comparison of Text Classification methods Method of weighted terms selected by different Stemming Techniques | |
Sri Lalitha et al. | Semantic Framework to Text Clustering with Neighbors | |
Aswini et al. | Pattern discovery for text mining | |
Belmouhcine et al. | Implicit links based web page representation for web page classification | |
Sani et al. | Term similarity and weighting framework for text representation | |
Chen et al. | Deep Image Annotation and Classification by Fusing Multi-Modal Semantic Topics. | |
Kumar et al. | IRISM@ NTCIR-12 Temporalia Task: Experiments with MaxEnt, Naive Bayes and Decision Tree Classifiers. | |
Wang et al. | LSA-PTM: a propagation-based topic model using latent semantic analysis on heterogeneous information networks | |
Long et al. | WordNet-based lexical semantic classification for text corpus analysis | |
Spanakis et al. | DoSO: a document self-organizer | |
Passos et al. | Wordnet-based metrics do not seem to help document clustering |