Letter Frequency Analysis of Languages Using Latin Alphabet (original) (raw)
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Frequency Analysis of Languages Using Latin Alphabet
2018
The evaluation of the peculiarities of alphabets, particularly the frequency of letters is essential when designing keyboards, analysing texts, designing alphabet-based games, and doing some text mining. Thus, it is important to determine what might be useful for designers of text input tools, and of other technologies related to sets of letters. Knowledge of common features among different languages gives an opportunity to take advantage of the experience of other languages. Nowadays an increasing amount of texts is published on the Internet. In order to adequately compare the frequencies of letters in different languages used in the online space, Wikipedia texts have been selected as a source material for investigation. This paper presents the Method of the Adjacent Letter Frequency Differences in the frequency line, which helps to evaluate frequency breakpoints. This is a uniform evaluation criterion for 25 main languages using Latin script in order to highlight the similarities ...
This research paper present some statistical similarities and dissimilarities between the character frequencies of three languages, Malay, Indonesia and English. The reason for their comparison is that they all share a common symbol set (A-Z). It has been found, through investigations that statistically Malay and Indonesian language character frequencies are very close to each other. For example, character "A" "N" and "E" in both Malay and Indonesian languages have frequencies (19%, 20.4%), (10%, 9.33%) and (9%, 8.28%), respectively. However, the case of English is different, where characters "E", "T" and "A" come with three highest frequency occurring letters, respectively. An interesting observation is that in spite of some similarities and dissimilarities between the characters, all three language follow envelop of the frequencies identically rising and falling trend for all characters. Moreover, for all three languages, last four characters, "W, X, Y, Z", also exhibit lowest usage in their respective languages.
Keyboard Layout Analysis: Creating the Corpus, Bigram Chains, and Shakespeare's Monkeys
The process to create a corpus suitable for evaluating computer keyboard layouts optimised for typing English and computer program code. After sourcing, sampling and cleaning suitable texts, the texts are processed to extract bigrams, which are then used to create sample input texts of a desired length. These texts have a character distribution, and letter sequence, closely matching either English or computer programs, even though they look random. The resulting texts are excellent for evaluating keyboard layouts. Corpus analysis is included. Keywords: English text corpus, computer code corpus, English letter frequency, computer program character frequency, bigram frequency, letter follows letter probability, letter precedes letter probability, keyboard layout, keyboard layout evaluation. Best viewed and printed in colour.
Contemporary Advancements in Information Technology Development in Dynamic Environments
The purpose of this chapter is to present current research on the modern Bulgarian language. It is one of the oldest European languages. An information system for the management of the electronic archive with texts in Bulgarian language is described. It provides the possibility for processing the collected text information. The detailed and comprehensive researches on the letter and the word frequency in the modern Bulgarian language from varied sources (fiction, scientific and popular science literature, press, legal texts, government bulletins, etc.) are performed, and the obtained results are represented. The index of coincidence of the Bulgarian language as a whole and for the individual sources is computed. The results can be utilized by different specialists – computer scientists, linguists, cryptanalysts, and others. Furthermore, with mathematical modeling, the authors found the letter and word frequency distributions and their models and they estimated their standard deviati...
Différenciation entre Alphabets dans des Textes Manuscrits
Notre but est de différencier et d'identifier des textes manuscrits écrits dans différents alphabets. Nous parvenons à notre but grâce à l'analyse fractale du style de l'écriture. Pour chaque alphabet, un ensemble de caractéristiques qui s'appuie sur les propriétés d'autosimilarité présentes dans l'écriture est extrait. Pour ce faire, des formes invariantes caractérisant l'écriture sont extraites par le processus de la compression fractale pendant la phase d'apprentissage. Elles sont ensuite organisées dans une base de référence qui peut être associée à un alphabet. L'étape d'identification d'alphabets est basée sur un processus de Pattern Matching utilisant successivement les différentes bases de références. Les résultats de cette analyse sont estimés par le coefficient de corrélation entre l'image initiale du texte et celle reconstruite à partir des différentes bases de références.
Two frequency-rank law for letters printed in Romanian
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Frequency Analysis of the Portuguese Language
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The study of a language statistics it is very important for the cryptanalysis of substitution and/or permutation ciphers. In that type of ciphers one letter is substituted by another one, or its order is changed, with the order of another letter also from the text. In either cases the "personality" of the letter remains intact, hidden inside a different vest, but intact anyway.
This is an analysis of letter frequencies in texts, where it has been proven that clearer text is less informative. It is better directed, has better-organized thoughts, is more effective in interpretation, and like an individual who submits to social organization, has reduced freedoms.
Creating a Corpus and Chained Bigrams for Spanish Keyboard Development and Evaluation
The process to create a corpus suitable for evaluating computer keyboard layouts optimised for typing Spanish. After sourcing, sampling and cleaning suitable texts, the texts are processed to extract bigrams, which are then used to create sample input texts of a desired length. These texts have a character distribution, and letter sequence, closely matching Spanish, even though they look random. The resulting texts are excellent for evaluating keyboard layouts. Corpus analysis is included.