Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique

Textual data is increasing exponentially and to extract the required information from the text, different techniques are being researched. Some of these techniques require the data to be presented in the tabular or matrix format. The proposed approach designs the Document Term Matrix for Marathi (DTMM) corpus and converts unstructured data into a tabular format. This approach has been called DTMM in this paper and it fails to consider the semantics of the terms. We propose another approach that forms synsets and in turn reduces dimensions to formulate a Document Synset Matrix for Marathi (DSMM) corpus. This also helps in better capturing the semantics and hence is context-based. We abbreviate and call this approach as DSMM and carry out experiments for document-similarity measurement on a corpus consisting of more than 1200 documents, consisting of both verses as well as proses, of Marathi language of India. Marathi text processing has been largely an untouched area. The precision, recall, accuracy, F1-score and error rate are used to prove the betterment of the proposed technique.

@article{Bafna2020,
  title     = {Marathi Document: Similarity Measurement using Semantics-based Dimension Reduction Technique},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {4},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Prafulla B. Bafna and Jatinderkumar R. Saini},
  doi       = {10.14569/IJACSA.2020.0110419},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110419}
}