A simple algorithm for the problem of suffix stripping

Abstract

Suffix stripping is a problem of removing morphological suffixes from a word to get the stem. We present suffix stripping as an unconstrained optimization problem. Free from linguistic or morphological knowledge, a simple algorithm is being developed. Superiority of the algorithm over an established technique for English language is being demonstrated. Suffix stripping ist der Prozess des systematischen Entfernens von Suffixen um zum Stamm zu gelangen. Wir präsentieren Suffix Stripping als ein Optimierungs problem ohne Nebenbedingungen. Ein einfacher Algorithmus jenseits linguistischen oder morphologischen Wissens wird entwickelt. Damit wird der Vorrang des Algorithmus vor einer Technik der englischen Sprache demonstriert. Stichwort: information wiedergewinnung, stemming, verschmelzung, stammeln, bearbeiten entfernung bs_bs_banner International Journal of Applied Linguistics ◆

Simultaneous Removal of Prefix and Suffix

“…To compare our approach with some standard algorithms from the literature, we also applied the following three stemming treatments over the sample being studied: Porter's algorithm, 7 Lancaster algorithm 8 and Approx-stem. 26 Columns 6, 8 and 10 of Table 3 present the outcomes of the above algorithms and columns 7, 9 and 11 record the lengths of corresponding stems, respectively. To implement Lancaster algorithm, 8 we employed a standard package: NLTK 3.4.5 in Python.…”

“…33 Nonparametric tests like Wilcoxon test have been used by various authors in the literature to evaluate the performance of their algorithms. [19][20][21]26 We choose to apply Kruskal-Wallis test for multiple samples. This test is the nonparametric alternative to the one-way ANOVA.…”

“…17 Corpus-based stemming, 18 Single N-gram stemming, 19 stemming based on Hidden Markov Model, 20,21 YASS 22 are some benchmarks in the domain of statistical stemming. Few recent researches in this¯eld are as follows: Peng et al, 23 Paik et al, 24 Pande et al, [25][26][27] Nikitas, 28 Tamta et al, 29 Patel and Shah. 30 Mixed algorithms generally combine multiple approaches.…”

API Assisted Semantic Matching for Subjective Online Examination System

“…This process of breaking user supplied answers into distinct word and comparing the standard keyword to user supplied answer is the most crucial part of the subjective examination process. Along with the standard keywords, the synonyms of the keywords submitted by the user will also be extracted and tokenized using the Porters Algorithm [11]. The tokenized keywords are the words that provide the key concept which are of significance in a document.…”