Discriminating the Registers and Styles in the Modern Greek Language-Part 1: Diglossia in Stylistic Analysis

Tambouratzis, George

doi:10.1093/llc/19.2.197

Cited by 7 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, the document collection used here is extracted from a specific domain and the variety of topics is limited, our aim being to group the documents so that the stylistic characteristics of the authors (and thus their identities) are revealed. This task has been found via statistical techniques to be more difficult than differentiating between documents based on their topics [9]. Hence, though the size of our corpus is smaller than the one used in the WEBSOM, the results are considered to be of a high quality, indicating the effectiveness of the proposed feature set as well as of the SOM model.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…Details on the parameter-extraction process are provided in [9]. The features may be divided into five groups, which are briefly reviewed in the remainder of this section.…”

Section: B Linguistic Features Studiedmentioning

confidence: 99%

“…Therefore, it is of interest to determine both 1) the effectiveness of the lemma features as well as 2) whether the remaining linguistic features (which form the majority of features) contribute to the accuracy of the final clustering. Earlier experiments [9], using statistical methods such as discriminant analysis, have shown that when combining several groups of features, a more accurate discrimination of authors is achieved. The experiments described in the remainder of the article are intended to determine whether this holds for an unsupervised clustering approach.…”

Section: ) Comments On the Features Usedmentioning

confidence: 99%

“…In these studies, originally statistical methods were used [9], [10], since they possessed solid theoretical foundations for the classification tasks at hand and, being supervised, were expected to provide the highest possible classification accuracy. Following that, SOM-based techniques were evaluated in the same tasks, in order to indicate the effectiveness of biologically inspired unsupervised methods in this task [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessing the effectiveness of feature groups in author recognition tasks with the SOM model

Tambouratzis

2006

IEEE Trans. Syst., Man, Cybern. C

Self Cite

View full text Add to dashboard Cite

The present paper focuses on studying the effectiveness of the self-organizing map (SOM) when applied to the task of categorizing a corpus of texts according to the style of their authors. This task is of particular importance for information retrieval applications using very large databases of documents. The emphasis of this article is to determine the extent to which the SOM possesses the ability to analyze such data, successfully uncovering the stylistic differences among authors in an unsupervised manner. To that end, a variety of feature vectors are studied, each of which either 1) comprises a single category of linguistic features or 2) spans several different categories of linguistic features, in order to determine the effectiveness of each feature category. It is shown that the highest accuracy is achieved when using a vector covering multiple linguistic categories. A comparison of the results obtained to the results of statistical methods indicates the ability of the SOM network to reveal the clustering potential of isolated parameter groups and its effectiveness in handling efficiently high-dimensional data vectors. Potential extensions to related text-organization techniques, such as the WEBSOM, thus become evident.

show abstract

Section: Discussion Of Resultsmentioning

confidence: 99%

“…Details on the parameter-extraction process are provided in [9]. The features may be divided into five groups, which are briefly reviewed in the remainder of this section.…”

Section: B Linguistic Features Studiedmentioning

confidence: 99%

Section: ) Comments On the Features Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing the effectiveness of feature groups in author recognition tasks with the SOM model

Tambouratzis

2006

IEEE Trans. Syst., Man, Cybern. C

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is considered to be among the most effective techniques in cases with a priori defined classes and has been successfully used in the studies of literary texts authorship detection (Baayen, van Halteren, & Tweedie, 1996;Mikros, 2006;Stamatatos, Fakotakis, & Kokkinakis, 2001;Tambouratzis et al, 2004), genre differentiation (Karlgen & Cutting, 1994;Murata, 2000) and for other purposes.…”

Section: Methodsmentioning

confidence: 99%

Patterns in Style Evolution of Poets

Andreev

2019

Sage Open

View full text Add to dashboard Cite

This article investigates the problem of style stability and variation over time and presents the results of analysis of style evolution of American romantic poet H.W. Longfellow. The feature set includes 29 morphological and syntactic characteristics. The method of multivariate discriminant analysis is used. The study showed significant style changes over time and made it possible to single out the major tendencies of style evolution: the poet in his mature verses returns to the characteristics of his early lyrics. Besides, comparison of the variable and stable characteristics demonstrated that the initial part of the line is connected with style evolution, whereas the end of the line (which is the rhymed position) is less variable over time. The results may be useful for authorship attribution and manuscript dating research.

show abstract

A register variation perspective on varieties of English

Neumann

Evert²

2021

Studies in Corpus Linguistics

View full text Add to dashboard Cite

This chapter reports an exploration of dimensions of register variation across varieties of English. We analyse 2,844 texts from the Hong Kong, Jamaica and New Zealand components of the International Corpus of English, using its text categorization scheme as a frame of reference. We apply Geometric Multivariate Analysis, an interactive procedure for exploring latent structure in language variation, based on the frequencies of 41 lexico-grammatical features informed by systemic functional register theory. Visual inspection of the distribution of texts across the multidimensional space reveals continuities between groups of texts as well as dimensions of variation that can be related to theoretical register constructs. We also observe differences between the three ICE components (and their text categories) in register space.

show abstract

Discriminating the Registers and Styles in the Modern Greek Language-Part 1: Diglossia in Stylistic Analysis

Cited by 7 publications

References 0 publications

Assessing the effectiveness of feature groups in author recognition tasks with the SOM model

Assessing the effectiveness of feature groups in author recognition tasks with the SOM model

Patterns in Style Evolution of Poets

A register variation perspective on varieties of English

Contact Info

Product

Resources

About