A Feature-based Stochastic Morphological Analyzer for Filipino Affixed Words

This paper papers presents a featured-based stochastic stemming methods for obtaining affixes in Filipino language. The method aims to introduce a statistical stemming approach that is based on the morphological attributes of Filipino words. Various Filipino word forms from different types of sources were obtained and test for affix removal system. The stemmer initially performs lexicon check from the created lexis which is comprises of common based words and various categorical language forms. Feature examinations are executed to check the data entry's structure. These includes affix removal, word assimilation, partial duplication, derivational words, and inflectional words. A KSTEM assimilatory method from Hybrid Stemming Algorithm are utilized to support derivational and inflectional conditions. From the created stochastic featured-based template algorithm, the entries were analyzed and perform the final phase of the stemming process. An average of 92.46 percent was obtained using the test data and stemming technique.