Sensed-Lexicon based Approach for Identification of Similarity among Punjabi Documents

Textual similarity among documents often leads to copyright issues. Manual measurement of similarity among documents is a time consuming infeasible activity. In this paper, we proposed a technique for measuring similarity at sensed-lexicon level for documents written in Punjabi language using Gurumukhi script. 50 Punjabi document pairs were manually collected with the help of Punjabi native writers. The proposed technique consisted of major 4 levels. Level 0 consists of data collection phase. Level 1 consists of noise removal and stop word removal sub levels. Extracted tokens were stemmed, lemmatized and synonyms were replaced based on part of speech tagging in level 2. Vector space representation corresponding to each document leads to n-gram generation of documents in level 2. Extracted n-grams were weighted based on term frequency. In level 3, string based token level similarity indexes such as Jaccard Similarity Index (JSI), Cosine Similarity Index (CSI) and Levenshtien Distance Index (LDI) were experimented with weighed tokens. In this work, Human Intelligence Task (HIT) based rating has been utilized for measuring the similarity among documents between 0-100. Results obtained from HIT based rating are compared with results obtained from the proposed technique with various combinations of pre-processing levels. Results revealed that on the basis of majority voting, combination of stop word removal with stemming and ‘noun’ based synonym replacement leads to the best combination with bi-gram tokens. Statistical analysis indicates strong correlation between CSI and HIT based rating.

@article{Kaur2021,
  title     = {Sensed-Lexicon based Approach for Identification of Similarity among Punjabi Documents},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {5},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Jasleen Kaur and Jatinderkumar R Saini},
  doi       = {10.14569/IJACSA.2021.0120565},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120565}
}