Результаты поиска по 'LaTeX':
Найдено статей: 2
  1. Chuvilin K.V.
    An efficient algorithm for ${\mathrm{\LaTeX}}$ documents comparing
    Computer Research and Modeling, 2015, v. 7, no. 2, pp. 329-345

    The problem is constructing the differences that arise on ${\mathrm{\LaTeX}}$ documents editing. Each document is represented as a parse tree whose nodes are called tokens. The smallest possible text representation of the document that does not change the syntax tree is constructed. All of the text is splitted into fragments whose boundaries correspond to tokens. A map of the initial text fragment sequence to the similar sequence of the edited document corresponding to the minimum distance is built with Hirschberg algorithm A map of text characters corresponding to the text fragment sequences map is cunstructed. Tokens, that chars are all deleted, or all inserted, or all not changed, are selected in the parse trees. The map for the trees formed with other tokens is built using Zhang–Shasha algorithm.

    Keywords: automation, editing distance, text analysis, lexeme, machine learning, metric, parse tree, syntax tree, token, ${\mathrm{\LaTeX}}$.
    Views (last year): 2. Citations: 2 (RSCI).
  2. Chuvilin K.V.
    The use of syntax trees in order to automate the correction of LaTeX documents
    Computer Research and Modeling, 2012, v. 4, no. 4, pp. 871-883

    The problem is to automate the correction of LaTeX documents. Each document is represented as a parse tree. The modified Zhang-Shasha algorithm is used to construct a mapping of tree vertices of the original document to the tree vertices of the edited document, which corresponds to the minimum editing distance. Vertex to vertex maps form the training set, which is used to generate rules for automatic correction. The statistics of the applicability to the edited documents is collected for each rule. It is used for quality assessment and improvement of the rules.

    Keywords: automation, editing distance, LaTeX, lexeme, machine learning, metric, parse tree, regular expression, reinforcement learning, syntax tree, text analysis, token.
    Citations: 5 (RSCI).

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