A Graph-based Text Similarity Measure That Employs Named Entity Information

Abstract

Text comparison is an interesting though hard task, with many applications in Natural Language Processing.This work introduces a new text-similarity measure, which employs named-entities' information extracted from the texts and the ngram graphs' model for representing documents.Using OpenCalais as a namedentity recognition service and the JIN-SECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means).The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.