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

Nagoudi, El Moatez Billah; Ferrero, Jérémy; Schwab, Didier; Cherroun, Hadda

doi:10.1007/978-3-319-73500-9_2

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Their proposed methods' performance was established through the Pearson correlation among both their allocated semantic similarity measures and human judgments. They actually achieved the highest correlation value compared with all of the participating systems in SemEval-2017's cross-language STS Arabic-English subtask [48].…”

Section: Literature Reviewmentioning

confidence: 96%

State of the art document clustering algorithms based on semantic similarity

Jacksi¹,

Salih²

2020

Jurnal Informatika

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Section: Literature Reviewmentioning

confidence: 96%

State of the art document clustering algorithms based on semantic similarity

Jacksi¹,

Salih²

2020

Jurnal Informatika

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“…After the fine-tuning step, this difference increased to 84.3% for precision and 88.5% for Fscore. Nagoudi et al [28] proposed a CL-PD system based on two WE approaches to compare the semantic text similarity of sentences in Arabic and English. The idea is to grasp the syntactic and semantic properties of the words by employing machine translation (MT) and word embedding.…”

Section: Related Workmentioning

confidence: 99%

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Bouaine,

Faouzia Benabbou,

Imane Sadgali

2023

Int. J. Interact. Mob. Technol.

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Academic plagiarism has become a serious concern as it leads to the retardation of scientific progress and violation of intellectual property. In this context, we make a study aiming at the detection of cross-linguistic plagiarism based on Natural language Preprocessing (NLP), Embedding Techniques, and Deep Learning. Many systems have been developed to tackle this problem, and many rely on machine learning and deep learning methods. In this paper, we propose Cross-language Plagiarism Detection (CL-PD) method based on Doc2Vec embedding techniques and a Siamese Long Short-Term Memory (SLSTM) model. Embedding techniques help capture the text's contextual meaning and improve the CL-PD system's performance. To show the effectiveness of our method, we conducted a comparative study with other techniques such as GloVe, FastText, BERT, and Sen2Vec on a dataset combining PAN11, JRC-Acquis, Europarl, and Wikipedia. The experiments for the Spanish-English language pair show that Doc2Vec+SLSTM achieve the best results compared to other relevant models, with an accuracy of 99.81%, a precision of 99.75%, a recall of 99.88%, an f-score of 99.70%, and a very small loss in the test phase.

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“…Also, in [32] Nagoudi and Schwab proposed a combination of word embedding and word alignment techniques and then calculated sentence embedding as a sum of its content of word vectors to tackle the Arabic STS problem [9]. Also in [33], Nagoudi et al proposed a sentence vectors-based method for…”

Section: The State-of-the-artmentioning

confidence: 99%

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning

et al. 2022

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Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.

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

Cited by 13 publications

References 30 publications

State of the art document clustering algorithms based on semantic similarity

State of the art document clustering algorithms based on semantic similarity

Word Embedding for High Performance Cross-Language Plagiarism Detection Techniques

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning

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