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Genre classification means to discriminate between documents by means of their form, their style, or their targeted audience. Put another way, genre classification is orthogonal to a classification based on the documents’ contents. While most of the existing investigations of an automated genre classification are based on news articles corpora, the idea(More)
We present a new approach to crosslanguage text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce taskspecific, cross-lingual word correspondences. We report on analyses that reveal(More)
We present an evaluation framework for plagiarism detection.1 The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon’s Mechanical Turk. We discuss the construction principles behind the(More)
This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling. To improve the reproducibility of shared tasks in general, and PAN’s tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other(More)
A consecutive series of 23 patients underwent operative removal of an intramedullary spinal cord ependymoma between January, 1976, and September, 1988. Thirteen women and 10 men between the age of 19 and 70 years experienced symptoms for a mean of 34 months preceding initial diagnosis. Eight patients had undergone treatment prior to tumor recurrence and(More)
We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they(More)
Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized(More)
The 1st International Competition on Plagiarism Detection, held in conjunction with the 3rd PAN workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse, brought together researchers from many disciplines around the exciting retrieval task of automatic plagiarism detection. The competition was divided into the subtasks external plagiarism(More)