{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:38:01Z","timestamp":1778168281643,"version":"3.51.4"},"reference-count":9,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:p>In the modern world, structured and semi-structured knowledge bases hold a considerable amount of data. There-fore, people who are familiar with formal query languages should not be the only ones who can efficiently and clearly query them. Semantic Parsing (SP) is converting natural language utterances into formal meaning representations. The paper suggests a model for SP that uses a novel method of utilizing the Semi-Supervised Generative Adversarial Network (SS-GAN) to enhance the classifier performance. The proposed SS-GAN extends the fine-tuning of word embedding architectures using unlabeled examples in a generative adversarial environment. We provide a regularization strategy for addressing the mode missing problem and unstable training in SS-GAN. The main viewpoint is to use the extracted feature vectors from the discriminator. Hence, the generator produces outputs by aiding the discriminator\u2019s learned features. A reconstruction loss is added to the loss function of the SS-GAN to drive the genera-tor to reconstruct outputs from the discriminator\u2019s features, hence steering the generator toward actual data configurations. The proposed reconstruction loss improves the performance of SS-GAN, produces high-quality outputs, and may be combined with other regularization loss functions to improve the performance of diverse GANs. We employ BERT word embedding for our model, which can be included in a downstream task and fine-tuned as a model, while the pre-trained BERT model can capture various linguistic properties. We examine the suggested model using the WikiSQL and SparC datasets, and the analysis findings reveal our model outperforms its rivals. The findings from our experiments indicate that the need for labeled samples can be minimized, down to as few as 100 instances, while still achieving commendable classification outcomes.<\/jats:p>","DOI":"10.3233\/jifs-233212","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T13:33:12Z","timestamp":1706016792000},"page":"6577-6588","source":"Crossref","is-referenced-by-count":3,"title":["A semi-supervised learning approach for semantic parsing boosted by BERT word embedding"],"prefix":"10.1177","volume":"46","author":[{"given":"Yanbin","family":"Bu","sequence":"first","affiliation":[{"name":"School of Media Technology, Communication University of China, Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Media Technology, Communication University of China, Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxiu","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Media Technology, Communication University of China, Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Media Technology, Communication University of China, Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yandan","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Media Technology, Communication University of China, Nanjing, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-233212_ref11","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1177\/107769905303000401","article-title":"Cloze procedure: A new tool for measuring readability","volume":"30","author":"Taylor","year":"1953","journal-title":"Journalism Quarterly"},{"issue":"2","key":"10.3233\/JIFS-233212_ref14","first-page":"287","article-title":"Efficient GAN-based method for extractive summarization","volume":"10","author":"Moravvej","year":"2022","journal-title":"Journal of Electrical and Computer Engineering Innovations (JECEI)"},{"key":"10.3233\/JIFS-233212_ref15","unstructured":"Salimans T. , Goodfellow I. , Zaremba W. , Cheung V. , Radford A. and Chen X. , Improved techniques for training gans, , Advances in Neural Information Processing Systems 29 (2016)."},{"issue":"3","key":"10.3233\/JIFS-233212_ref17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1017\/S0269888900000588","article-title":"Natural language computing: the commercial applications","volume":"1","author":"Johnson","year":"1984","journal-title":"The Knowledge Engineering Review"},{"issue":"1","key":"10.3233\/JIFS-233212_ref19","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1017\/S135132490000005X","article-title":"Natural language interfaces to databases\u2013an introduction","volume":"1","author":"Androutsopoulos","year":"1995","journal-title":"Natural Language Engineering"},{"key":"10.3233\/JIFS-233212_ref22","first-page":"2014","article-title":"Sequence to sequence learning with neural networks,","volume":"27","author":"Sutskever","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/JIFS-233212_ref24","first-page":"2232","article-title":"Pointer graph networks,","volume":"33","author":"Velickovi\u0107","year":"2020","journal-title":"Advances inNeural Information Processing Systems"},{"key":"10.3233\/JIFS-233212_ref27","doi-asserted-by":"crossref","unstructured":"Marani M. , Soltani M. , Bahadori M. , Soleimani M. and Moshayedi A. , The Role of Biometric in Banking: A Review, EAI Endorsed Transactions on AI and Robotics 2(1) (2023).","DOI":"10.4108\/airo.3676"},{"key":"10.3233\/JIFS-233212_ref29","doi-asserted-by":"crossref","unstructured":"Soleimani M. , Forouzanfar Z. , Soltani M. and Harandi M.J. , Imbalanced multiclass medical data classification based on learning automata and neural network, , EAI Endorsed Transactions on AI and Robotics 2 (2023).","DOI":"10.4108\/airo.3526"}],"container-title":["Journal of Intelligent &amp; 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