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Simple Search
Hits 1 – 14 of 14
1
Multi-sense embeddings through a word sense disambiguation process ...
Ruas, Terry
;
Grosky, William
;
Aizawa, Aiko
. - : arXiv, 2021
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2
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
Stegmüller, Johannes
;
Bauer-Marquart, Fabian
;
Meuschke, Norman
. - : arXiv, 2021
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3
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
Stegmüller, Johannes
;
Bauer-Marquart, Fabian
;
Meuschke, Norman
. - : figshare, 2021
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4
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
Stegmüller, Johannes
;
Bauer-Marquart, Fabian
;
Meuschke, Norman
. - : figshare, 2021
BASE
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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
. - : figshare, 2021
BASE
Show details
7
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.v2
https://figshare.com/articles/preprint/Detecting_Cross-Language_Plagiarism_using_Open_Knowledge_Graphs/17212340/2
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8
Incorporating Word Sense Disambiguation in Neural Language Models ...
Wahle, Jan Philip
;
Ruas, Terry
;
Meuschke, Norman
. - : arXiv, 2021
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9
Enhanced word embeddings using multi-semantic representation through lexical chains ...
Ruas, Terry
;
Ferreira, Charles Henrique Porto
;
Grosky, William
. - : arXiv, 2021
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10
Enhanced word embeddings using multi-semantic representation through lexical chains ...
Ruas, Terry
;
Ferreira, Charles H. P.
;
Grosky, William
. - : University of Michigan, 2019
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11
Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains
Ruas, Terry L.
. - 2019
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12
Multi-sense Embeddings through a Word Sense Disambiguation Process
Ruas, Terry
;
Grosky, William
;
Aizawa, Akiko
. - 2018
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13
Semantic-Based Document Retrieval Using Spatial Distributions of Concepts ...
Ruas, Terry L.;Grosky, William I.
. - : University of Michigan, 2017
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14
Exploring and Expanding the Use of Lexical Chains in Information Retrieval
Ruas, Terry
;
Grosky, William
. - 2017
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