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doi:10.1002/asi.v60:3

Cited by 183 publications

(19 citation statements)

References 512 publications

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“…For example, it can be used for Entity Linking [15] for information retrieval in question answering. It can also be used in automatic text summarization or author identification [16]. Trend detection and hot topic extraction in news feeds and linguistic content based social media, like Twitter, are other use-cases where disambiguation is required.…”

Section: Motivationmentioning

confidence: 99%

Semi-Supervised Multiple Disambiguation

Ghoorchian¹,

Rahimian²,

Girdzijauskas³

2015

2015 IEEE Trustcom/BigDataSE/Ispa

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Abstract-Determining the true entity behind an ambiguous word is an NP-Hard problem known as Disambiguation. Previous solutions often disambiguate a single ambiguous mention across multiple documents. They assume each document contains only a single ambiguous word and a rich set of unambiguous context words. However, nowadays we require fast disambiguation of short texts (like news feeds, reviews or Tweets) with few context words and multiple ambiguous words. In this research we focus on Multiple Disambiguation (MD) in contrast to Single Disambiguation (SD). Our solution is inspired by a recent algorithm developed for SD. The algorithm categorizes documents by first, transferring them into a graph and then, clustering the graph based on its topological structure. We changed the graph-based document-modeling of the algorithm, to account for MD. Also, we added a new parameter that controls the resolution of the clustering. Then, we used a supervised sampling approach for merging the clusters when appropriate. Our algorithm, compared with the original model, achieved 10% higher quality in terms of F1-Score using only 4% sampling from the dataset.

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Section: Motivationmentioning

confidence: 99%

Semi-Supervised Multiple Disambiguation

Ghoorchian¹,

Rahimian²,

Girdzijauskas³

2015

2015 IEEE Trustcom/BigDataSE/Ispa

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“…In a verification problem (see above) one is given writing examples of an author A, and one is asked to verify whether or not a document d of unknown authorship in fact is written by A. Recent contributions to the authorship attribution problem include (Rudman 1997;Stamatatos 2001Stamatatos , 2007Stamatatos , 2009Chaski 2005;Juola 2006;Malyutov 2006;Sanderson and Guenter 2006b); the authorship verification problem is addressed in Koppel and Schler (2004b), van Halteren (2004van Halteren ( , 2007, Meyer zu Eissen and Stein (2006Stein ( , 2007, Koppel et al (2007), , Stein et al 2008 andPavelec et al (2008).…”

Section: Existing Researchmentioning

confidence: 99%

“…Average word frequency class* Meyer zu Eissen and Stein (2004) Syntactic features Part-of-speech Stamatatos (2009) and Koppel (2009) Part-of-speech n-gram frequency/ratio* Koppel and Schler (2003) and Koppel (2009) Frequency of function words* Mosteller and Wallace (1964), Holmes …”

Section: Outlier Identificationmentioning

confidence: 99%

“…,Argamon et al (2003), Schler (2003), Zheng et al greetings and farewells Zheng et al (2006) andStamatatos (2009) Use of signaturesZheng et al (2006) andStamatatos (2009) group (= plagiarized). Given a document d and a single style features x, the maximum a-posteriori hypothesis H [ {S t , S o } can be determined with Bayes' rule: H ¼ argmax S2fS t ;S o g PðxðsÞ j SÞ Á PðSÞ PðxðsÞÞ…”

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confidence: 99%

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Intrinsic plagiarism analysis

Stein

Lipka

Prettenhofer

2010

Lang Resources & Evaluation

109

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Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art.(2) We show how the meta learning approach of Koppel and Schler, termed ''unmasking'', can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning. Problem statementIn the following, the term plagiarism refers to text plagiarism, i.e., the use of another author's information, language, or writing, when done without proper acknowledgment of the original source. Plagiarism detection refers to the unveiling of text plagiarism. Existing approaches to computer-based plagiarism detection break down this task into manageable parts:''Given a text d and a reference collection D, does d contain a section s for which one can find a document d i [ D that contains a section s i such that under some retrieval model R the similarity u R between s and s i is above a threshold h?''Observe that research on automated plagiarism detection presumes a closed world where a reference collection D is given. Since D can be extremely largepossibly the entire indexed part of the World Wide Web-the main research focus is on efficient search technology: near-similarity search and near-duplicate detection (Brin et al

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“…It is known that design engineers spend approximately 25% of their time searching for and using design information to complete their work [1]. However, it is difficult to find a required document among thousands of them considering the graphic nature of technical documentation, which includes drawings, 3D models and scanned documents containing both text and graphics.…”

Section: Introductionmentioning

confidence: 99%