An Unsupervised Learning Embedding Method Based on Semantic Hashing | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 2، شماره 3 - شماره پیاپی 9، زمستان 2022، صفحه 39-46 اصل مقاله (1.05 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2023.30651.1124 | ||
| نویسندگان | ||
| Javad Hamidzadeh* 1؛ Mona Moradi2 | ||
| 1Faculty of computer engineering and information technology, Sadjad University, Mashhad, Iran. | ||
| 2Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| چکیده | ||
| Embedding learning is an essential issue in Natural Language Processing (NLP) applications. Most existing methods measure the similarity between text chunks in a context using pre-trained word embedding. However, providing labeled data for model training is costly and time-consuming. So, these methods face downward performance when limited amounts of training data are available. This paper presents an unsupervised sentence embedding method that effectively integrates semantic hashing into the Kernel Principal Component Analysis (KPCA) to construct embeddings of lower dimensions that can be applied to any domain. The experiments conducted on benchmark datasets highlighted that the generated embeddings are general-purpose and can capture semantic meanings from both small and large corpora. | ||
| کلیدواژهها | ||
| Kernel Principal Component Analysis؛ Natural Language Processing؛ Semantic Hashing؛ Sentence Embedding | ||
| مراجع | ||
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