Development for performance of Porter stemmer algorithm
DOI:
https://doi.org/10.15587/1729-4061.2021.225362Keywords:
stemming algorithm, natural language processing, information retrieval, APSA, Porter algorithmAbstract
The Porter stemmer algorithm is a broadly used, however, an essential tool for natural language processing in the area of information access. Stemming is used to remove words that add the final morphological and diacritical endings of words in English words to their root form to extract the word root, i.e. called stem/root in the primary text processing stage. In other words, it is a linguistic process that simply extracts the main part that may be close to the relative and related root. Text classification is a major task in extracting relevant information from a large volume of data. In this paper, we suggest ways to improve a version of the Porter algorithm with the aim of processing and overcome its limitations and to save time and memory by reducing the size of the words. The system uses the improved Porter derivation technique for word pruning. Whereas performs cognitive-inspired computing to discover morphologically related words from the corpus without any human intervention or language-specific knowledge. The improved Porter algorithm is compared to the original stemmer. The improved Porter algorithm has better performance and enables more accurate information retrieval (IR).
References
- Seddiqui, H., Maruf, A. A. M., Chy, A. N. (2016). Recursive Suffix Stripping to Augment Bangla Stemmer. ICAICT-2016-Paper. Available at: http://www.ciu.edu.bd/icaict2016/publications/ICAICT-2016-Paper%20(50).pdf
- Shah, F. P., Patel, V. (2016). A review on feature selection and feature extraction for text classification. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). doi: https://doi.org/10.1109/wispnet.2016.7566545
- Saeed, A. M., Rashid, T. A., Mustafa, A. M., Agha, R. A. A.-R., Shamsaldin, A. S., Al-Salihi, N. K. (2018). An evaluation of Reber stemmer with longest match stemmer technique in Kurdish Sorani text classification. Iran Journal of Computer Science, 1 (2), 99–107. doi: https://doi.org/10.1007/s42044-018-0007-4
- Agbele, K., Adesina, A., Azeez, N., Abidoye, A. (2012). Context-Aware Stemming algorithm for semantically related root words. African Journal of Computing & ICT, 5 (4), 33–42.
- Akkus, B. K., Cakici, R. (2013). Categorization of Turkish News Documents with Morphological Analysis. 51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop. Sofia, 1–8. Available at: https://www.aclweb.org/anthology/P13-3001.pdf
- Kumar, R., Mansotra, V. (2016). Applications of stemming algorithms in information retrieval-a review. International Journal of Advanced Research in Computer Science and Software Engineering, 6 (2), 418–423.
- Biba, M., Gjati, E. (2014). Boosting Text Classification through Stemming of Composite Words. Recent Advances in Intelligent Informatics, 185–194. doi: https://doi.org/10.1007/978-3-319-01778-5_19
- Farrar, D., Huffman Hayes, J. (2019). A Comparison of Stemming Techniques in Tracing. 2019 IEEE/ACM 10th International Symposium on Software and Systems Traceability (SST). doi: https://doi.org/10.1109/sst.2019.00017
- Al-Sharhan, S., Al-Hunaiyyan, A., Alhajri, R., Al-Huwail, N. (2019). Utilization of Learning Management System (LMS) Among Instructors and Students. Advances in Electronics Engineering, 15–23. doi: https://doi.org/10.1007/978-981-15-1289-6_2
- Joshi, A., Thomas, N., Dabhade, M. (2016). Modified Porter Stemming Algorithm. International Journal of Computer Science and Information Technologies, 7 (1), 266–269.
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Copyright (c) 2021 Manhal Elias Polus, Thekra Abbas
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