The rapid increase in the quantity of Kurdish documents over the last several years has created a need for improving information accuracy and precision in text classification and retrieval. Language stemming is an imperative pre-processing step for increasing the possibility of matching terms in a document in text classification tasks. Stemming helps reduce the total number of searchable terms within a document or query. This article proposes an active approach for stemming Kurdish Sorani texts to reduce variations of words to single terms or stems. The outcomes of the process, described in this article, demonstrate that decreasing the dimensionality of feature vectors in documents will increase the effectiveness of retrieval when the stemming process is used. This process applied for Kurdish Sorani can be adapted and applied in Kurdish Kurmanji as well for greater efficiency and effectiveness in digital text classification and applications.
TashakoriMMeybodiMOroumchianF.Bon: first Persian stemmer. In: ShafazandHMin TjoaA (eds.) Lecture notes on information and communication technology (LNCS). Berlin: Springer, 2002, pp. 487–494.
5.
NaouarFHlaouaLOmriMN.Possibilistic model for relevance feedback in collaborative information retrieval. Int J Web Appl2012; 4(2): 78–86.
6.
BoukhariKOmriMN.Robust algorithm for stemming text document. Int J Comput Inform Syst Ind Manage Appl2016; 8: 235–246.
7.
EsmailiKSSalavatiSEliassiDet al. Building a test collection for Sorani Kurdish. In: Proceedings of the 10th ACS/IEEE international conference on computer systems and applications (AICCSA’13), Ifrane, Morocco, 27–30 May 2013. New York: IEEE.
SamvelianP. A lexical account of Sorani Kurdish prepositions. In: MullerS (ed.) Proceedings of international conference on head-driven phrase structure grammar. Stanford, CA: CSLI Publications, 2007, pp. 235–249.
12.
TairMABarakaRS.Design and evaluation of a parallel classifier for large-scale Arabic text design and evaluation of a parallel classifier for large-scale Arabic text. Int J Comput Appl2015; 75(3): 12–20.
13.
XuJCroftWB.Corpus-based stemming using co-occurrence of word variants. ACM T Inform Syst1998; 16(1): 61–81.
14.
LovinsJB.Development of a stemming algorithm. Mech Transl Comput Linguist1968; 11(1–2): 22–31.
15.
JivaniGA.A comparative study of stemming algorithms. Int J Comput Technol Appl2011; 2(6): 1930–1938.
16.
RobertK.Viewing morphology as an inference process. In: Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval, Pittsburgh, PA, 27 June–1 July 1993, pp. 191–202. New York: ACM.
17.
PengFAhmedNLiXet al. Context sensitive stemming for web search. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, Amsterdam, 23–27 July 2007, pp. 639–646. New York: ACM.
18.
ChenAGeyF. Building an Arabic stemmer for information retrieval. In: Proceedings of the 11th text retrieval conference (TREC), November 2002, pp. 19–22. Gaithersburg, MD: NIST.
19.
IngasonAKHelgadóttirSLoftssonH.A mixed method lemmatization algorithm using a hierarchy of linguistic identities (HOLI) (ed. RantaANordströmB). Berlin: Springer, 2008, pp. 205–216.
20.
DarwishK.Building a shallow Arabic morphological analyzer in one day. In: Proceedings of the workshop on computational approaches to Semitic languages in the 40th annual meeting of the association for computational linguistics (ACL-02), Philadelphia, PA, 7–12 July 2002, pp. 47–54. New York: ACM.
21.
SalavatiSEsmailiKSAkhlaghianF. Stemming for Kurdish information retrieval. In: Proceedings of the 9th Asia information retrieval societies conference, Singapore, 9–11 December 2013, vol. 8281, pp. 272–283. Berlin: Springer.
22.
JayanthiR.An approach for effective text pre-processing using improved Porters stemming algorithm. Int J Innov Sci Eng Technol2015; 2(7): 797–807.
23.
DilekhTBehloulA.Implementation of a new hybrid method for stemming of Arabic text. Int J Comput Appl2012; 46(8): 14–19.
24.
PaiceCD. An evaluation method for stemming algorithms. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval, Dublin, 3–6 July 1994, pp. 42–50. New York: ACM.
25.
KraaijWPohlmannR. Porter’s stemming algorithm for Dutch. In: Informatiewetenschap 1994: Wetenschappelijkebijdragenaan de derde STINFON Conferentie (ed.NoordmanLGMde VroomenWAM), 1994, pp. 167–180. Tilburg, Netherlands.
26.
KaraaWBAGribâaN. Information retrieval with Porter Stemmer: a new version for English. In: NagamalaiDKumaAAnnamalaiA (eds) Advances in computational science, engineering and information technology, vol. 225. Heidelberg: Springer, 2013, pp. 243–254.
27.
HmeidiIAl-AyyoubMAbdullaNAet al. Automatic Arabic text categorization: a comprehensive comparative study. J Inform Sci2014; 41(1): 114–124.
28.
FloresFNMoreiraVPHeuserCA. Assessing the impact of stemming accuracy on information retrieval. In: Proceedings of the international conference on computational processing of the Portuguese language, Porto Alegre, Brazil, 27–30 April 2010, pp. 11–20. Berlin: Springer.
29.
PaiceCD.Method for evaluation of stemming algorithms based on error counting. J Am Soc Inform Sci1996; 47(8): 632–649.
30.
GuptaVJoshiNMathurI. Design & development of rule based inflectional and derivational Urdu stemmer ‘Usal’. In: 2015 International conference on futuristic trends in computational analysis and knowledge management (INBUSH ERA-2015), Noida, India, 25–27 February 2015, pp. 7–12. New York: IEEE.
31.
ThaljiNJ.Developing an effective light stemmer for Arabic language information retrieval. Int J Comput Inform Technol2016; 5(1): 55–59.
32.
KumarDRanaP.Stemming of Punjabi words by using brute force technique. Int J Eng Sci Technol2011; 3(2): 1351–1358.
33.
SinghJGuptaV.A systematic review of text stemming techniques. Artif Intell Rev. Epub ahead of print 1 August 2016. DOI: 10.1007/s10462-016-9498-2.
