An AST method for scoring string-to-text similiarity in semantic text analysis

Миркин Б. Г., Артемова Е. Л.

Abstract. A suffix-tree based method for measuring similarity of a key phrase to an unstructured text is proposed. The measure involves less computation and it does not depend on the length of the text or the key phrase. This applies to the following tasks in semantic text analysis:

1. Finding interrelations between key phrases over a set of texts;

2. Annotating a research article by topics from a taxonomy of the domain;

3. Clustering relevant topics and mapping clusters on a domain taxonomy. 

 

Ключевые слова: suffix tree, unstructured text analysis, string-to-text similarity measures, суффиксное дерево

ПУБЛИКАЦИЯ ПОДГОТОВЛЕНА ПО РЕЗУЛЬТАТАМ ПРОЕКТА: Методы визуализации текстовой информации с помощью построения суффиксных деревьев, мультифасетных классификаций и иерархических онтологий: алгоритмическое и программное обеспечение (2013)

В книге

Clusters, orders, trees: methods and applications. In Honor of Boris Mirkin's 70th Birthday

Vol. 92. , Berlin: Springer, 2014.