Recursive Center Embedding: An Extension of MLCE for Semantic Evaluation of Complex Sentences

A novel method for representing hierarchical sentences, named Multi-Leveled Center Embedding (MLCE), has recently been introduced. The approach utilizes the concept of center-embedded structures to demonstrate the structural complexity of complex sentences through iterative calculations of differences between the original and modified embeddings of its hierarchy. Through an implementation of Recursive Center-Embedding (RCE), we enhance the concept of MLCE by incorporating additional leveled features from the center-word hierarchy. The features are essential for training the Word2Vec model, enabling it to generate sophisticated vectors that perform well in sentence similarity analysis. RCE produces vectors via a hier-archical arrangement of center components, illustrating sentence structure that exceeds that of traditional word vectors and the BERT-base contextual model. The aim is to assess the similarity performance of the proposed RCE strategy. Furthermore, it examines its contextual ability obtained through leveled feature vectors that successfully correlated pairs of complex sentences across multiple benchmark datasets.

@article{Dubey2024,
  title     = {Recursive Center Embedding: An Extension of MLCE for Semantic Evaluation of Complex Sentences},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {ShivKishan Dubey and Narendra Kohli},
  doi       = {10.14569/IJACSA.2024.0151289},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151289}
}