This paper presents a knowledge-based method for measuring the semanticsimilarity of texts. While there is a large body of previous work focused on finding the semantic similarity of concepts and words, the application of these wordoriented methods to text similarity has not been yet explored. In this paper, we introduce a method that combines wordto-word similarity metrics into a text-totext m...

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-li...

Text similarity measures have been widely studied and used in machine learning and information retrieval for many years. However, few applications of text similarity have dealt with multi-lingual translations of a specific document. Additionally, the growing number of texts with more translations being generated increases the challenge of distinguishing or identifying the similarity and differe...

Correct and efficient text classification is a major challenge in today’s world of rapidly increasing amount of accessible electronic text data. Kohonen networks have been applied to document classification with comparable success to other document clustering methods. An important challenge is to devise text similarity metrics that can improve the performance of text classification Kohonen netw...

Quantifying the similarity or dissimilarity between documents is an important task in authorship attribution, information retrieval, plagiarism detection, text mining, and many other areas of linguistic computing. Numerous similarity indices have been devised and used, but relatively little attention has been paid to calibrating such indices against externally imposed standards, mainly because ...

We present DKPro Similarity, an open source framework for text similarity. Our goal is to provide a comprehensive repository of text similarity measures which are implemented using standardized interfaces. DKPro Similarity comprises a wide variety of measures ranging from ones based on simple n-grams and common subsequences to high-dimensional vector comparisons and structural, stylistic, and p...

Thread disentanglement is the task of separating out conversations whose thread structure is implicit, distorted, or lost. In this paper, we perform email thread disentanglement through pairwise classification, using text similarity measures on non-quoted texts in emails. We show that i) content text similarity metrics outperform style and structure text similarity metrics in both a class-balan...

One of the success factors of Work Integrated Learning (WIL) is to provide the appropriate content to the users, both suitable for the topics they are currently working on, and their experience level in these topics. Our main contributions in this paper are (i) overcoming the problem of sparse content annotation by using a network based recommendation approach called Associative Network, which ...

Text Analytics is essential for a large number of applications and good approaches to obtain visual mappings of text are paramount. Many visualization techniques, such as similarity based point placement layouts, have proved useful to support visual analysis of documents. However, they are sensitive to data quality, which, in turn, relies on a critical preprocessing step that involves text ‘cle...

For the purpose of opinion exploring in tweets, this article presents a sentiment classification of tweets content. First, we present a method to identify new sentiment similarity seed words. These seed words are used for predicting sentiment intensity of other words and short phrases in co-occurrence. Then, for testing sentiment similarity, we use: Similarity Measures methods between words and...