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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Multi-sense embeddings through a word sense disambiguation process ... Ruas, Terry; Grosky, William; Aizawa, Aiko. - : arXiv, 2021 BASE Show details 2 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman. - : arXiv, 2021 BASE Show details 3 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman. - : figshare, 2021 BASE Show details 4 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman. - : figshare, 2021 BASE Show details 5 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman. - : figshare, 2021 BASE Show details 6 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman; Lima Ruas, Terry; Schubotz, Moritz; Gipp, Bela. - : figshare, 2021 Abstract: Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application to Web-scale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA's performance in terms of the well-established ... Keyword: 80107 Natural Language Processing; FOS Computer and information sciences URL: https://dx.doi.org/10.6084/m9.figshare.17212340.v3 https://figshare.com/articles/preprint/Detecting_Cross-Language_Plagiarism_using_Open_Knowledge_Graphs/17212340/3 BASE Hide details 7 Detecting Cross-Language Plagiarism using Open Knowledge Graphs ... Stegmüller, Johannes; Bauer-Marquart, Fabian; Meuschke, Norman. - : figshare, 2021 BASE Show details 8 Incorporating Word Sense Disambiguation in Neural Language Models ... Wahle, Jan Philip; Ruas, Terry; Meuschke, Norman. - : arXiv, 2021 BASE Show details 9 Enhanced word embeddings using multi-semantic representation through lexical chains ... Ruas, Terry; Ferreira, Charles Henrique Porto; Grosky, William. - : arXiv, 2021 BASE Show details

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