Clustering Based on Context Similarity

Kovács, László; Repasi, Tibor; Baksa-Varga, Erika; Barabás, Péter

doi:10.1109/cans.2008.26

Cited by 2 publications

(3 citation statements)

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“…In both cases, the degree of objectivity (or subjectivity) varies depending on both the text length (long texts tend to be more detailed) and number of descriptive words (a text with many adjectives for instance tends to be very detailed). Equally important seems to be the context induced by words [2,3], since words inducing restricted contexts somehow limit the ability to extrapolate ideas, and this makes the text more objective.…”

Section: Introductionmentioning

confidence: 99%

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Using complex networks to quantify consistency in the use of words

2012

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In this paper we quantify the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood. Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according to the consistency of use, the words obeyed a log-normal distribution, in contrast to the Zipfs law that applies to the frequency of use. Consistency correlated positively with the familiarity and frequency of use, and negatively with ambiguity and age of acquisition. An inspection of some highly consistent words confirmed that they are used in very limited semantic contexts. A comparison of consistency indices for 8 authors indicated that these indices may be employed for author recognition. Indeed, as expected authors of novels could be distinguished from those who wrote scientific texts. Our analysis demonstrated the suitability of the consistency indices, which can now be applied in other tasks, such as emotion recognition.

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Section: Introductionmentioning

confidence: 99%

“…Parameters obtained from fitting the consistency probability density functions. All distributions follow a log-normal distribution, since R 2 ∼ 1 and χ2 1.…”

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confidence: 99%

Using complex networks to quantify consistency in the use of words

2012

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“…Later, clustering by context has been introduced as a working approach to evade the problems caused in clustering by content (Attardi, Di Marco, & Salvi, 1998;Attardi, Gulli, & Sebastiani, 1999;Flanagan, 2005;Kovacs, Repasi, Baksa-Varga, & Barabas, 2008;Mirkin, 2005;Pedrycz, 2005), Clustering by context is based on grouping web pages whereby the context surrounding a link is used for categorizing the document referred by the link. The conception is based on the assumption that a web page that refers to a document must necessarily involve enough hints about its content which themselves are sufficient to classify the document (Attardi et al, 1998;Pedrycz, 2005).…”

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confidence: 99%

Towards a Stylometric Authorship Recognition Model for the Social Media Texts in Arabic

Alsager¹

2020

AWEJ

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Numerous studies have been concerned with developing new authorship recognition systems to address the increasing rates of cybercrimes associated with the anonymous nature of social media platforms, which still offer the opportunity for the users not to reveal their true identities. Nevertheless, it is still challenging to identify the real authors of social media’s offensive and inappropriate content. These contents are usually very short; therefore, it is challenging for stylometric authorship systems to assign controversial texts to their real authors based on the salient and distinctive linguistic features and patterns within these contents. This research introduces a new stylometric authorship system that considers both the shortness of data and the peculiar linguistic properties of Arabic. A corpus of 20, 357 tweets from 134 Twitter users. A document clustering based on Document Index Graph (DIG) model was used to classify input patterns in the tweets that shared common linguistic features. A comparative analysis using Vector Space Clustering (VSC) model based on the Bag of Words (BOW) model, conventionally used in authorship recognition applications, was used. Results indicate that the proposed system is more accurate than other standard authorship systems mainly based on vector space clustering methods. It was also clear that the model had the advantage of providing complete information about the documents and the degree of overlap between every pair of documents, which was useful in determining the similarity between documents.

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Clustering Based on Context Similarity

Cited by 2 publications

References 5 publications

Using complex networks to quantify consistency in the use of words

Using complex networks to quantify consistency in the use of words

Towards a Stylometric Authorship Recognition Model for the Social Media Texts in Arabic

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