1 |
Multi-sense embeddings through a word sense disambiguation process ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
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.v4 https://figshare.com/articles/preprint/Detecting_Cross-Language_Plagiarism_using_Open_Knowledge_Graphs/17212340/4
|
|
BASE
|
|
Hide details
|
|
6 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Detecting Cross-Language Plagiarism using Open Knowledge Graphs ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Incorporating Word Sense Disambiguation in Neural Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Enhanced word embeddings using multi-semantic representation through lexical chains ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Enhanced word embeddings using multi-semantic representation through lexical chains ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Semantic Feature Extraction Using Multi-Sense Embeddings and Lexical Chains
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Multi-sense Embeddings through a Word Sense Disambiguation Process
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Semantic-Based Document Retrieval Using Spatial Distributions of Concepts ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Exploring and Expanding the Use of Lexical Chains in Information Retrieval
|
|
|
|
BASE
|
|
Show details
|
|
|
|