Lexical Ambiguity in Natural Language Processing Applications

Harsha, N. Sree; Kumar, Ch. Nageswar; Sonthi, Vijaya Krishna; Amarendra, K

doi:10.1109/icears53579.2022.9752297

Cited by 4 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of Bhadwal et al (2020) utilized Rule-Based Machine Translation (RBMT) to translate prominent language features of Hindi and Sanskrit, effectively addressing the challenge of polysemy in verb translation. While RBMT could produce high-quality translation, these systems needed improvement in handling ambiguity and idiosyncrasies of new language pairs or domains (Harsha et al, 2022;De Martino et al, 2023). The advent of statistical methods, notably with IBM's Candide system and Google Translate, marked a significant advancement of MT from RBMT (Jumanto et al, 2022).…”

Section: Evolution Of Machine Translationmentioning

confidence: 99%

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Ghassemiazghandi

2024

tpls

View full text Add to dashboard Cite

Traditional views have long held that machine translation cannot achieve the quality and accuracy of human translators, especially in complex language pairs like Persian and English. This study challenges this perspective by demonstrating that ChatGPT-4, with access to vast amounts of multilingual data and leveraging advanced large language model algorithms, significantly outperforms widely utilized open-source machine translation tools and approaches the realm of human translation quality. This research aims to critically assess the translation accuracy of ChatGPT-4 against a traditional open-source machine translation tool from Persian to English, highlighting the advancements in artificial intelligence-driven translation technologies. Using Bilingual Evaluation Understudy scores for a comprehensive evaluation, this study compares the translation outputs from ChatGPT-4 with MateCat, providing a quantitative basis for comparing their accuracy and quality. ChatGPT-4 achieves a BLUE score of 0.88 and an accuracy of 0.68, demonstrating superior performance compared to MateCat, with a 0.82 BLUE score and 0.49 accuracy. The results indicate that the translations generated by ChatGPT-4 surpass those produced by MateCat and nearly mirror the quality of human translations. The evaluation demonstrates the effectiveness of OpenAI's large language model algorithms in improving translation accuracy.

show abstract

Section: Evolution Of Machine Translationmentioning

confidence: 99%

An Evaluation of ChatGPT's Translation Accuracy Using BLEU Score

Ghassemiazghandi

2024

tpls

View full text Add to dashboard Cite

show abstract

“…A domain vocabulary, essential concepts with their taxonomy, relationships (and constraints) between concepts, and domain axioms are defined for specific program applications [8] [9]. Thus, using ontologies to express requirement specifications has implications for advantage in managing complexity, contradictions, or detecting ambiguity and incompleteness of requirements [4][10] [11]. The application of ontology to requirement specification can help to eliminate the problem of erroneous test case generation from ambiguous, inconsistent, or incomplete requirements.…”

Section: Introductionmentioning

confidence: 99%

An Approach of Test Case Generation with Software Requirement Ontology

Intana,

Tantayakul,

Laosen

et al. 2023

IJACSA

View full text Add to dashboard Cite

Software testing plays an essential role in software development process since it helps to ensure that the developed software product is free from errors and meets the defined specifications before the delivery. As the software specification is mostly written in the form of natural language, this may lead to the ambiguity and misunderstanding by software developers and results in the incorrect test cases to be generated from this unclear specification. Therefore, to solve this problem, this paper presents a novel hybrid approach, Software Requirement Ontologies based Test Case Generation (ReqOntoTestGen) to enhance the reliability of existing software testing techniques. This approach enables a framework that combines ontology engineering with the software test case generation approaches. Controlled Natural Language (CNL) provided by the ROO (Rabbit to OWL Ontologies Authoring) tools is used by the framework to build the software requirement ontology from unstructured functional requirements. This eliminates the inconsistency and ambiguity of requirements before test case generation. The OWL ontology resulted from ontology engineering is then transformed into the XML file of data dictionary. Combination of Equivalence and Classification Tree Method (CCTM) is used to generate test cases from this XML file with the decision tree. This allows us to reduce redundancy of test cases and increase testing coverage. The proposed approach is demonstrated with the developed prototype tool. The contribution of the tool is confirmed by the validation and evaluation result with two real case studies, Library Management System (LMS) and Kidney Failure Diagnosis (KFD) Subsystem, as we expected.

show abstract