Supervised Learning to Measure the Semantic Similarity Between Arabic Sentences

Wali, Wafa; Gargouri, Bilel; Hamadou, Abdelmajid Ben

doi:10.1007/978-3-319-24069-5_15

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Another shortcoming of previous studies focusing on the Arabic language is that the semantic similarity analyses employed have not utilized enough resources (e.g., tools and benchmark data) due to a lack of availability. One such research work was conducted by [15], who determined semantic similarity at the sentence level using supervised learning. Specifically, their method analyzed semantic, lexical, and syntactic-semantic features, which were extracted using an Arabic dictionary, a lexical markup framework, and a learning corpus.…”

Section: Related Workmentioning

confidence: 99%

NGram Approach for Semantic Similarity on Arabic Short Text

Al-Mahmoud¹,

Sharieh²

2022

IJACSA

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Measuring the semantic similarity between words requires a method that can simulate human thought. The use of computers to quantify and compare semantic similarities has become an important research area in various fields, including artificial intelligence, knowledge management, information retrieval, and natural language processing. Computational semantics require efficient measures for computing concept similarity, which still need to be developed. Several computational measures quantify semantic similarity based on knowledge resources such as the WordNet taxonomy. Several measures based on taxonomical parameters have been applied to optimize the expression for content semantics. This paper presents a new similarity measure for quantifying the semantic similarity between concepts, words, sentences, short text, and long text based on NGram features and Synonyms of NGram related to the same domain. The proposed algorithm was tested on 700 tweets, and the semantic similarity values were compared with cosine similarity on the same dataset. The results were analyzed manually by a domain expert who concluded that the values provided by the proposed algorithm were better than the cosine similarity values within the selected domain regarding the semantic similarity between the datasets' short texts.

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Section: Related Workmentioning

confidence: 99%

NGram Approach for Semantic Similarity on Arabic Short Text

Al-Mahmoud¹,

Sharieh²

2022

IJACSA

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“…Based on what Wali et al [46] discussed, we noted that most of the previous researches mentioned above estimated the semantic similarity based only on the word order or the syntactic dependency and the synonymy relationship between terms in sentences without taking into consideration the semantic arguments namely the semantic class and thematic role in computing the semantic similarity. Wali et al [46] presented a hybrid method for measuring semantic similarity between sentences depending on supervised learning and three linguistics features (Lexical, Semantic and Syntactic-Semantic) extracted from learning corpus and Arabic dictionaries like LMF dictionary. This is a two-phase method: the learning phase, which consists of two processes: the pre-processing process that aimed to have an annotated corpus and the training process that is used to catch a hyperplane equation via the learning algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Textual Similarity Measurement Approaches: A Survey (1)

Abo-Elghit

Al-Zoghby

Hamza

2020

The Egyptian Journal of Language Engineering

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Survey research is appropriate and necessary to address certain research question types. This paper aims to provide a general overview of the textual similarity in the literature. Measuring textual similarity tends to have an increasingly important turn in related topics like text classification, recovery of specific information from data, clustering, topic retrieval, subject tracking, question answering, essay grading, summarization, and the nowadays trending Conversational Agents (CA), which is a program deals with humans through natural language conversation. Finding the similarity between terms is the essential portion of textual similarity, then used as a major phase for sentence-level, paragraph-level, and script-level similarities. In particular, we concern with textual similarity in Arabic. In the Arabic Language, applying Natural language Processing (NLP) tasks are very challenging indeed as it has many characteristics, which are considered as confrontations. However, many approaches for measuring textual similarity have been presented for Arabic text reviewed and compared in this paper.

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“…Following the trend of combining detection methods, we see the analysis of non-textual content features as a promising component of future integrated detection approaches. Surprisingly many papers in our collection addressed plagiarism detection for Arabic and Persian texts (e.g., References [22,118,231,262]). The interest in plagiarism detection for the Arabic language led the organizers of the PAN competitions to develop an Arabic corpus for intrinsic plagiarism detection [34].…”

Section: Extrinsic Plagiarism Detectionmentioning

confidence: 99%

“…In 2015, the PAN organizers also introduced a shared task on plagiarism detection for Arabic texts [32], followed by a shared task for Persian texts one year later [22]. While these are promising steps toward improving plagiarism detection for Arabic, Wali et al [262] noted that the availability of corpora and lexicons for Arabic is still insufficient when compared to other languages. This lack of resources and the complex linguistic features of the Arabic language cause plagiarism detection for Arabic to remain a significant research challenge [262].…”

Section: Extrinsic Plagiarism Detectionmentioning

confidence: 99%

Untitled

2019

CSUR

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This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Specialized information retrieval; • Computing methodologies → Natural language processing; Machine learning approaches; • Applied computing → Digital libraries and archives;

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Supervised Learning to Measure the Semantic Similarity Between Arabic Sentences

Cited by 13 publications

References 5 publications

NGram Approach for Semantic Similarity on Arabic Short Text

NGram Approach for Semantic Similarity on Arabic Short Text

Textual Similarity Measurement Approaches: A Survey (1)

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