Cross-Language High Similarity Search Using a Conceptual Thesaurus

Gupta, Parth; Barrón-Cedeño, Alberto; Rosso, Paolo

doi:10.1007/978-3-642-33247-0_8

Cited by 25 publications

(25 citation statements)

References 14 publications

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“…For that, a vector of concepts is built for each textual unit using dictionaries or thesaurus. The similarity between the vectors of concepts can be measured using the Cosine similarity, Euclidean distance, or any MT-Based Models Kent and Salim [18], Muhr et al [29], SS-CL-AES [3], CL-PDAE [2] Comparable Corpora-Based Models CL-KGA [11], CL-ESA [12] Parallel Corpora-Based Models CL-ASA [6], CL-LSI [35], CL-KCCA [42], CL-AE-LSI [17] Dictionary-Based Models CL-CTS [15], CL-DBLI [32], CL-PDAE [2] Syntax-Based Models Length Model [16], CL-CNG [22] Fig. 1: Taxonomy of different approaches for cross-language similarity detection [10].…”

Section: Cross-language Semantic Textual Similarity Detectionmentioning

confidence: 99%

“…In [15] a Cross-Language Conceptual Thesaurus-Based Similarity model (CL-CTS) is proposed to measure the similarity between textual units written in different languages (Spanish, English and German). CL-CTS is based on the thesaurus concepts vectors presented in Eurovoc 1 where a Cosine similarity is computed between these vectors.…”

Section: Cross-language Semantic Textual Similarity Detectionmentioning

confidence: 99%

“…There are two known types of STS: monolingual and cross-language [3]. The first one estimates the degree to which the underlying semantics of two textual units written in the same language, are equivalent to each other, while the STS cross-language aims to quantify the degree to which two textual units are semantically related, independent of the languages they are written in [15].…”

Section: Introductionmentioning

confidence: 99%

“…While cross-language semantic similarity is relatively more difficult to identify since the relatedness of words are investigated between two different languages [15]. Thus, it is necessary to address this task to enhance the performance in several applications, such as Machine Translation (MT), Cross-Language Plagiarism Detection (CLPD) and Cross-Language Information Retrieval (CLIR).…”

Section: Introductionmentioning

confidence: 99%

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Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

Nagoudi

Ferrero

Schwab

et al. 2018

Communications in Computer and Information Science

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Abstract. Semantic Textual Similarity (STS) is an important component in manyNatural Language Processing (NLP) applications, and plays an important role in diverse areas such as information retrieval, machine translation, information extraction and plagiarism detection. In this paper we propose two word embeddingbased approaches devoted to measuring the semantic similarity between ArabicEnglish cross-language sentences. The main idea is to exploit Machine Translation (MT) and an improved word embedding representations in order to capture the syntactic and semantic properties of words. MT is used to translate English sentences into Arabic language in order to apply a classical monolingual comparison. Afterwards, two word embedding-based methods are developed to rate the semantic similarity. Additionally, Words Alignment (WA), Inverse Document Frequency (IDF) and Part-of-Speech (POS) weighting are applied on the examined sentences to support the identification of words that are most descriptive in each sentence. The performances of our approaches are evaluated on a crosslanguage dataset containing more than 2400 Arabic-English pairs of sentence. Moreover, the proposed methods are confirmed through the Pearson correlation between our similarity scores and human ratings.

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Section: Cross-language Semantic Textual Similarity Detectionmentioning

confidence: 99%

Section: Cross-language Semantic Textual Similarity Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

Nagoudi

Ferrero

Schwab

et al. 2018

Communications in Computer and Information Science

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“…The cross-language alignment-based similarity analysis (CL-ASA) model [3,2] is instead based on a statistical machine translation technology that combines probabilistic translations, using a statistical bilingual dictionary and similarity analysis. Finally, the cross-language conceptual thesaurus based similarity (CL-CTS) model [8] tries to measure the similarity between the documents in terms of shared concepts, using a conceptual thesaurus, and named entities among them. Some of these models have been compared in detecting CL plagiarism in [14].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graphs as Context Models: Improving the Detection of Cross-Language Plagiarism with Paraphrasing

Franco-Salvador

Gupta

Rosso

2014

Lecture Notes in Computer Science

Self Cite

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Abstract. Cross-language plagiarism detection attempts to identify and extract automatically plagiarism among documents in different languages. Plagiarized fragments can be translated verbatim copies or may alter their structure to hide the copying, which is known as paraphrasing and is more difficult to detect. In order to improve the paraphrasing detection, we use a knowledge graph-based approach to obtain and compare context models of document fragments in different languages. Experimental results in German-English and Spanish-English crosslanguage plagiarism detection indicate that our knowledge graph-based approach offers a better performance compared to other state-of-the-art models.

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CLEU ‐ A Cross‐language english‐urdu corpus and benchmark for text reuse experiments

Muneer¹,

Sharjeel

Iqbal

et al. 2018

Asso for Info Science & Tech

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Text reuse is becoming a serious issue in many fields and research shows that it is much harder to detect when it occurs across languages. The recent rise in multi-lingual content on the Web has increased cross-language text reuse to an unprecedented scale. Although researchers have proposed methods to detect it, one major drawback is the unavailability of large-scale gold standard evaluation resources built on real cases. To overcome this problem, we propose a cross-language sentence/passage level text reuse corpus for the English-Urdu language pair. The Cross-Language English-Urdu Corpus (CLEU) has source text in English whereas the derived text is in Urdu. It contains in total 3,235 sentence/passage pairs manually tagged into three categories that is near copy, paraphrased copy, and independently written. Further, as a second contribution, we evaluate the Translation plus Mono-lingual Analysis method using three sets of experiments on the proposed dataset to highlight its usefulness. Evaluation results (f 1 =0.732 binary, f 1 =0.552 ternary classification) indicate that it is harder to detect cross-language real cases of text reuse, especially when the language pairs have unrelated scripts. The corpus is a useful benchmark resource for the future development and assessment of cross-language text reuse detection systems for the English-Urdu language pair.

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Cross-Language High Similarity Search Using a Conceptual Thesaurus

Cited by 25 publications

References 14 publications

Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

Knowledge Graphs as Context Models: Improving the Detection of Cross-Language Plagiarism with Paraphrasing

CLEU ‐ A Cross‐language english‐urdu corpus and benchmark for text reuse experiments

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