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https://olympias.lib.uoi.gr/jspui/handle/123456789/11065Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kalogeratos, Argyris | en |
| dc.contributor.author | Likas, Aristidis | en |
| dc.date.accessioned | 2015-11-24T17:02:32Z | - |
| dc.date.available | 2015-11-24T17:02:32Z | - |
| dc.identifier.issn | 0219-1377 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11065 | - |
| dc.rights | Default Licence | - |
| dc.subject | Text mining | en |
| dc.subject | Document clustering | en |
| dc.subject | Semantic matrix | en |
| dc.subject | Data projection | en |
| dc.title | Text document clustering using global term context vectors | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | 10.1007/s10115-011-0412-6 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2011 | - |
| heal.abstract | Despite the advantages of the traditional vector space model (VSM) representation, there are known de?ciencies concerning the term independence assumption. The high dimensionality and sparsity of the text feature space and phenomena such as polysemy and synonymy can only be handled if a way is provided to measure term similarity. Many approaches have been proposed that map document vectors onto a new feature space where learning algorithms can achieve better solutions. This paper presents the global term context vector-VSM (GTCV-VSM) method for text document representation. It is an extension to VSM that: (i) it captures local contextual information for each term occurrence in the term sequences of documents; (ii) the local contexts for the occurrences of a term are combined to de?ne the global context of that term; (iii) using the global context of all terms a proper semantic matrix is constructed; (iv) this matrix is further used to linearly map traditional VSM (Bag of Words BOW) document vectors onto a semantically smoothed feature space where problems such as text document clustering can be solved more ef?ciently. We present an experimental study demonstrating the improvement of clustering results when the proposed GTCV-VSM representation is used compared with traditional VSM-based approaches. | en |
| heal.journalName | Knowledge and Information Systems | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
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