Cross-Lingual Plagiarism Detection Method

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AK6ESGMHU" target="_blank" >RIV/00216208:11320/22:K6ESGMHU - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-12285-9_13" target="_blank" >https://doi.org/10.1007/978-3-031-12285-9_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-12285-9_13" target="_blank" >10.1007/978-3-031-12285-9_13</a>

Result language

angličtina

Original language name

Cross-Lingual Plagiarism Detection Method

Original language description

In this paper, we describe a method for cross-lingual plagiarism detection for a distant language pair (Russian-English). All documents in a reference collection are split into fragments of fixed size. These fragments are indexed in a special inverted index, which maps words to a bit array. Each bit in the bit array shows whether a $$i_{th}$$ithsentence contains this word. This index is used for the retrieval of candidate fragments. We employ bit arrays stored in the index for assessing similarity of query and candidate sentences by lexis. Before doing retrieval, top keywords of a query document are mapped from one language to other with the help of cross-lingual word embeddings. We also train a language-agnostic sentence encoder that helps in comparing sentence pairs that have few or no lexis in common. The combined similarity score of sentence pairs is used by a text alignment algorithm, which tries to find blocks of contiguous and similar sentence pairs. We introduce a dataset for evaluation of this task - automatically translated Paraplag (monolingual dataset for plagiarism detection). The proposed method shows good performance on our dataset in terms of F1. We also evaluate the method on another publicly available dataset, on which our method outperforms previously reported results.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Data Analytics and Management in Data Intensive Domains

ISBN

978-3-031-12285-9

ISSN

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e-ISSN

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Number of pages

16

Pages from-to

207-222

Publisher name

Springer International Publishing

Place of publication

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Event location

Cham

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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