University of Hertfordshire

By the same authors

Comparing Different Text Similarity Methods

Research output: Book/ReportOther report

Documents

View graph of relations
Original languageEnglish
PublisherUniversity of Hertfordshire
Publication statusPublished - 2007

Publication series

NameUH Computer Science Technical Report
PublisherUniversity of Hertfordshire
Volume461

Abstract

This paper reports experiments on a corpus of news articles from the Financial Times, comparing different text similarity models. First the Ferret system using a method based solely on lexical similarities is used, then methods based on semantic similarities are investigated. Different feature string selection criteria are used, for instance with and without synonyms obtained from WordNet, or with noun phrases extracted for comparison. The results indicate that synonyms rather than lexical strings are important for finding similar texts. Hypernyms and noun phrases also contribute to the identification of text similarity,--though they are not better than synonyms. However, precision is a problem for the semantic similarity methods because too many irrelevant texts are retrieved.

ID: 87182