an unsupervised learning embedding method based on semantic hashing

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.