摘要：One of the problems of computer corpus linguistics is an automatic determination of keywords inthe text. The solution is a statistical method based on calculation of various frequency characteristics of the text. In this case,the most commonly used model is a "bag of words”,which does not take into account the order of words in the text. In this paper,we propose a graph model of the text that allows us to calculate the frequency characteristics of words in the text not only within the framework of the "word bag” model,but with respect to location of pairs of owls in some common part of the text,for example,in one sentence. To work with such a model,a software model is constructed in the form of a database schema intended for storing various statistical text information. Taking into account such a data model,the article proposes an algorithm for determining the keywords of the text,the implementation of which is performed in the Python programming language. When analyzing a document d of linguistics corpus D,our algorithm creates a list of about 40 words with the largest measure tf-idf,and choise from them 20 words,which are more often used in the document d. We regard these words as vertices of some graph G,and the multiplicity of the edge,connecting the vertices t and t’ is equal to the number of sentences in document d,containing both these words. Approximately 10 vertices of the graph with the greatest degree are selected. The words corresponding to these vertices are taken for key words of document d.
其他摘要：Одной из задач компьютерной корпусной лингвистики является задача автоматического определения ключевых слов текста. Основные методы решения данной задачи,будучи статистическими, базируются на вычислении различных частотных характеристик текста. При этом ч
关键词：graph;text;word;text split;statistic measure tf-idf;key word;base form of word.
其他关键词：граф;текст;слово;разбиение текста;статистическая мера tf-idf;ключевое слово;базовая форма слова.
