Cluster Analysis for Corpus Linguistics

Moisl, Hermann

doi:10.1515/9783110363814

Cited by 49 publications

(30 citation statements)

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“…This raises the question of how the researcher can know in advance how many groups are optimal. The obvious solution is to base the initial decision on a priori knowledge (Moisl, ). As described above, previous analyses of the Dutch data tentatively identified three groups: a large ‘instrumental’ group and two smaller ‘anglophile’ and ‘anti‐English’ groups (author).…”

Section: Methodsmentioning

confidence: 99%

A cluster analysis of attitudes to English in Germany and the Netherlands

Edwards

Fuchs²

2018

World Englishes

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Although English is becoming increasingly entrenched in Western Europe, large‐scale comparative studies of attitudes among the general public to this development are scarce. We investigate over 4,000 Dutch and Germans’ attitudes towards English based on responses to an attitudinal questionnaire. Respondents saw English as a useful additional language, but not generally as a threat to their national language. Using k‐means, an unsupervised clustering algorithm, we identified two attitudinal groups per country. Respondents with positive attitudes towards English, regardless of nationality, tended to be younger, urban, better educated and more proficient in English than their compatriots with more negative views of English. These within‐country differences outweighed between‐country ones, for example, that Germans were more confident in the status of their L1, whereas Dutch showed signs of ‘English fatigue’. The findings thus appear to confirm the previously identified divide between elite ‘haves’ versus ‘have‐nots’ of English.

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Section: Methodsmentioning

confidence: 99%

A cluster analysis of attitudes to English in Germany and the Netherlands

Edwards

Fuchs²

2018

World Englishes

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“…The complex set of individual frequency differences is transformed into a compact measure of similarity between the samples. There are many possible mathematical transformations that can be used as distance measures (Moisl, 2014); stylometry uses a dozen or so of them.…”

Section: Multidimensional Methodsmentioning

confidence: 99%

An open stylometric system based on multilevel text analysis

Eder

Piasecki

Walkowiak

2017

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An open stylometric system based on multilevel text analysisStylometric techniques are usually applied to a limited number of typical tasks, such as authorship attribution, genre analysis, or gender studies. However, they could be applied to several tasks beyond this canonical set, if only stylometric tools were more accessible to users from different areas of the humanities and social sciences. This paper presents a general idea, followed by a fully functional prototype of an open stylometric system that facilitates its wide use through to two aspects: technical and research flexibility. The system relies on a server installation combined with a web-based user interface. This frees the user from the necessity of installing any additional software. At the same time, the system offers a variety of ways in which the input texts can be analysed: they include not only the usual lexical level, but also deep-level linguistic features. This enables a range of possible applications, from typical stylometric tasks to the semantic analysis of text documents. The internal architecture of the system relies on several well-known software packages: a collection of language tools (for text pre-processing), Stylo (for stylometric analysis) and Cluto (for text clustering). The paper presents: (1) The idea behind the system from the user’s perspective. (2) The architecture of the system, with a focus on data processing. (3) Features for text description. (4) The use of analytical systems such as Stylo and Cluto. The presentation is illustrated with example applications. Otwarty system stylometryczny wykorzystujący wielopoziomową analizę języka Zastosowania metod stylometrycznych na ogół ograniczają się do kilku typowych problemów badawczych, takich jak atrybucja autorska, styl gatunków literackich czy studia nad zróżnicowaniem stylistycznym kobiet i mężczyzn. Z pewnością dałoby się je z powodzeniem zastosować również do wielu innych problemów klasyfikacji tekstów, gdyby tylko owe metody oraz odpowiednie narzędzia były bardziej dostępne dla uczonych reprezentujących różne dyscypliny nauk humanistycznych i społecznych. Artykuł niniejszy omawia założenia teoretyczne oraz w pełni funkcjonalny prototyp otwartego systemu stylometrycznego, którego szerokie zastosowanie umożliwią dwie jego cechy: elastyczność techniczna oraz dostosowywalność do różnych pytań badawczych. System opiera się na instalacji serwerowej sprzęgniętej z sieciowym interfejsem użytkownika. Uwalnia to użytkownika od konieczności instalowania jakichkolwiek dodatkowych programów. Jednocześnie system oferuje wiele sposobów analizowania tekstów nie tylko na poziomie leksykalnym, lecz także poprzez cechy językowe niskiego poziomu. Daje to możliwość stosowania systemu na wiele różnych sposobów, od typowych testów stylometrycznych do analizy semantycznej dokumentów. Wewnętrzna architektura systemu składa się z wielu elementów znanych ze swej funkcjonalności, w tym z pakietu Stylo przeznaczonego do analiz stylometrycznych oraz pakietu Cluto służącego do zaawansowanej analizy skupień. Artykuł omawia: (1) Koncepcję całego systemu, postrzeganą z punktu widzenia użytkownika, (2) Architekturę systemu oraz jego elementy odpowiedzialne za przetwarzanie tekstu, (3) Cechy językowe służące do opisu dokumentów, (4) Zastosowanie modułów analizy danych, takich jak Stylo czy Cluto. W artykule zostały też przedstawione przykładowe zastosowania systemu.

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“…Depending on the amount of curvature, the difference between the two measures can be significant, and can therefore significantly affect analysis based on it. Given the difficulty of determining the presence of non-linearity in high-dimensional data and given also the implications of non-linearity for the present analysis cannot be ignored, and because hierarchical methods are linear, the additional method or methods used must be non-linear to take account of non-linearity and accommodate the possibility that the DFW, Dbigram, and Dtrigram contain significant non-linearities [33]. (2) It is recognized that a single class of methods cannot safely be relied on [34,54], and that at least one additional method or class of methods must be used to corroborate the results from hierarchical analysis.…”

Section: Cluster Analysis Methodsmentioning

confidence: 99%

“…Cluster analysis aims to detect and graphically to reveal structures or patterns in the distribution of data items, variables, or texts, in n-dimensional space, where n is the number of variables used to describe an author's style. There is a large number of cluster analysis methods and a large literature associated with each [33,34]. Apart from a few attempts using hierarchical cluster analysis methods and principal components analysis with authorship attribution [32,[35][36][37][38][39], to the best of my knowledge, until recently, little work has been done using cluster analytical methods with authorship attribution problems.…”

Section: Stylometrymentioning

confidence: 99%

“…The generic term "proximity" is used to cover both similarity and dissimilarity between and within pairs of vectors. Proximity between vectors can be measured in terms of their correlation, of angle between them, or distance in Euclidean space [33,34]. These are closely related, and if all the variables are measured on the same scale or have been standardized, there is no particular reason to prefer one over another.…”

Section: Hierarchical Cluster Analysismentioning

confidence: 99%

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Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to “Shakespeare Authorship Question”

Aljumily

2015

Social Sciences

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A few literary scholars have long claimed that Shakespeare did not write some of his best plays (history plays and tragedies) and proposed at one time or another various suspect authorship candidates. Most modern-day scholars of Shakespeare have rejected this claim, arguing that strong evidence that Shakespeare wrote the plays and poems being his name appears on them as the author. This has caused and led to an ongoing scholarly academic debate for quite some long time. Stylometry is a fast-growing field often used to attribute authorship to anonymous or disputed texts. Stylometric attempts to resolve this literary puzzle have raised interesting questions over the past few years. The following paper contributes to "the Shakespeare authorship question" by using a mathematically-based methodology to examine the hypothesis that Shakespeare wrote all the disputed plays traditionally attributed to him. More specifically, the mathematically based methodology used here is based on Mean Proximity, as a linear hierarchical clustering method, and on Principal Components Analysis, as a non-hierarchical linear clustering method. It is also based, for the first time in the domain, on Self-Organizing Map U-Matrix and Voronoi Map, as non-linear clustering methods to cover the possibility that our data contains significant non-linearities. Vector Space Model (VSM) is used to convert texts into vectors in a high dimensional space. The aim of which is to compare the degrees of similarity within and between limited samples of text (the disputed plays). The various works and plays assumed to have been written by Shakespeare and possible authors notably, Sir Francis Bacon, Christopher Marlowe, John Fletcher, and Thomas Kyd, where "similarity" is defined in terms of correlation/distance coefficient measure based on the frequency of usage profiles of function words, word bi-grams, and character triple-grams. The claim that Shakespeare authored all the disputed plays traditionally attributed to him is falsified in favor of the alternative authors according to the stylistic criteria and analytic methodology used. The result

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Cluster Analysis for Corpus Linguistics

Cited by 49 publications

References 0 publications

A cluster analysis of attitudes to English in Germany and the Netherlands

A cluster analysis of attitudes to English in Germany and the Netherlands

An open stylometric system based on multilevel text analysis

Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to “Shakespeare Authorship Question”

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