Preliminary evidence is provided that the two-level scheme is feasible as an initial semantic-similarity indicator for plagiarism screening tool for Indonesian-language student assignment documents, with the final judgment of plagiarism remaining with the examiner.
Abstract
Plagiarism remains a serious threat to academic integrity, while most available detection tools operate through literal word matching and are therefore easily defeated by paraphrasing. This study implements the pre-trained IndoBERT model without fine-tuning as a semantic-similarity screening tool for Indonesian-language student assignment documents, using a two-level scheme: a chunk-level similarity score (200 words with a 50-word overlap) as a document-level indicator, and one-to-one sentence alignment as a locator of similar passages. A 768-dimensional embedding is formed through attention-masked mean pooling followed by L2 normalization, and vectors are compared using cosine similarity at a 0.92 threshold. Evaluation was conducted on 20 pairs drawn from 15 documents, of which only 11 were authentic student submissions while 4 were controls derived from a single source document through graded paraphrasing. Similarity scores decreased across the four control conditions ordered by intended paraphrase intensity, namely 100%, 66.67%, 42.34%, and 26.67%, whereas same-theme original pairs averaged 5.93%. Against a proxy sentence-level ground truth, the system obtained 84.79% accuracy, 96.82% precision, 61.13% recall, and a 74.94% F1-score with a 1.19% false positive rate. A User Acceptance Testing exercise with six respondents yielded an index of 82.56%, describing only the participating group rather than general acceptance. These results provide preliminary evidence that the two-level scheme is feasible as an initial semantic-similarity indicator, with the final judgment of plagiarism remaining with the examiner.
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
Hanan Fawzy, Ahmad Salah, Heba El-Fiqi et al.· Informatica· 0 citations
This study investigates complex word identification (CWI) in Indonesian as a first step towards text simplification for dyslexic readers. This study uses CWI as a token-level classifier with IndoBERT, employing WordPiece tokenization and subword-to-word label alignment (only the first subword of each word receives a gold label; the rest are protected from label loss). To support a wide range of application needs, this study calibrates the decision threshold (τ) on development data and reports the results at multiple operating points to reveal the precision-recall trade-off. The model is fine-tuned on a token-level Indonesian corpus and evaluated on a held-out test set of 11.574 tokens (NonCWI = 10.769; CWI = 805). On this test set, IndoBERT-CWI delivers stable performance at three representative thresholds: τ = 0.40 (Precision 0.736, Recall 0.909, F1 0.814, Accuracy 0.971), τ = 0.46 (Precision 0.743, Recall 0.896, F1 0.812, Accuracy 0.971), and τ = 0.60 (Precision 0.771, Recall 0.856, F1 0.811, Accuracy 0.972). The precision-recall (PR) curve on the test set has an Average Precision of 0.783 and shows τ = 0.46 as a balanced operating point (highest F1), while τ = 0.40 is preferred when maximizing coverage (recall) is crucial to avoid missing complex tokens. A brief analysis shows that model errors occur most frequently when frequently occurring function words and proper nouns are marked as complex. This pattern is consistent with the characteristics of a news-based corpus and provides a practical suggestion that the simplification module should filter out entity names and function words from processing. Our results demonstrate that a well-tuned IndoBERT is effective for Indonesian CWI, with easily calibrated thresholds to balance precision–recall in dyslexia applications, and provide directions for improvement in further lexical simplification.
-. Lisnawita, Juhaida Abu Bakar, Ruziana Mohamad Rasli· JOIV: International Journal...· 0 citations
A Python implementation for comparing Kannada Unicode texts using sentence-level sequence similarity and character- and word-level bigram and trigram similarities and computes Jaccard and cosine n-gram scores, and produces an interpretable report.
P. U B, Dr. B. Prajna· Open Access Research Journal...· 0 citations
Computational approaches to intertextuality have advanced from string matching to neural retrieval, yet their outputs, similarity scores and parallel-passage lists, identify where texts reuse one another without characterizing how or why. We recast fine-grained intertextuality extraction as an agentic task in which a large language model (LLM) reads two text units in full and, through a constrained tool interface, must ground each proposed reuse in exact character spans on both sides and label it under a five-dimension typology of reuse (form, aspect, source-marking, function, stance). We validate the approach on an exhaustive comparison of the Analects with the Book of Han, where three domain experts adjudicate a pooled multi-model candidate set into a benchmark of 2,533 intertextual pairs. Against this standard we study twelve LLMs, reporting precision (56%-93%), a 51$\times$ cost spread at comparable quality, and how well their confidence is calibrated. Expert agreement traces a reliability gradient: dimensions legible on the textual surface are annotated consistently, while those requiring inference of intent are contested, delimiting the claims such annotation supports. Scaling the validated extractor to the full Twenty-Four Histories (65,380 comparisons, 5,766 pairs) recovers corpus-level structure a similarity score cannot express. The interpretive composition of citation shows no systematic change across eighteen centuries, yet the same passage is quoted less and less literally. Stability in the aggregate with drift in the individual case is what a cultural-attraction account expects. We release the extraction protocol and the expert-adjudicated benchmark.
Experimental results indicate that the proposed approach achieves competitive performance compared to existing plagiarism detection systems, and the comparative analysis highlights the strengths and limitations of different word embedding models across datasets.
Malya Singh, Vishal Gupta· Knowledge and Information Sy...· 0 citations
The results suggest that integrating diverse similarity measures with neural networks enhances the identification of both explicit and nuanced paraphrases, thereby supporting advancements in text analysis and plagiarism detection systems.
Emad Nabil· Islamic University Journal o...· 0 citations
