Overview: There are a variety of circumstances under which it would be useful to determine that two documents contain similar text, including detecting plagiarism and copyright infringement, and filtering and organizing documents returned by a search engine. The vast amount of digital information

www.ukp.tu-darmstadt.de While the concept of similarity is well grounded in psychology, text similarity is less well-defined. Thus, we analyze text similarity with respect to its definition and the datasets used for evaluation. We formalize text similarity based on the geometric model of conceptual

This paper describes TEXTSIM, a system for determining the similarity between texts. Further, we show the results of a comparison between two various configurations of TEXTSIM; one with and one without any deeper linguistic analysis. To evaluate and compare the two models of TEXTSIM we used two

Measuring the similarity between words, sentences, paragraphs and documents is an important component in various tasks such as information retrieval, document clustering, word-sense disambiguation, automatic essay scoring, short answer grading, machine translation and text summarization

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 inves-tigated. Different feature

Abstract. This paper presents a new method for recognizing the text entailment obtained from the text-to-text metric introduced in [3] and from the modified resolution introduced in [12]. In [11], using the directional measure of similarity as presented in [3], which measures the semantic

www.ukp.tu-darmstadt.de 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

The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to predict the semantic equivalence between a pair of sentences. The lexical metrics are based

If we are to use electronic plagiarism detectors on student work, it would be interesting to know how much similarity should be expected in independently written documents on a similar topic. If our measure is coarse, the answer should be zero, but a finer grained analysis (such as would be needed

Abstract. In this article we describe a new algorithm method for the detection of plagiarism. The method removes numerous limitations of our older method, which has been used as part of a complex information system for the detection of plagiarism. The method has been tested using multiple corpora mainly in Slovak language. With the PAN-09 and PAN-10 corpora it was of great advantage that we could compare our results with the results of other methods. The very good initial results gave us motivation to implement multiple algorithm and parameter improvements. 1