Stemming is a process in which the variant word forms are mapped to their base form. It is
among the basic text pre-processing approaches used in Language Modeling, Natural …

This article surveys work on Unsupervised Learning of Morphology. We define
Unsupervised Learning of Morphology as the problem of inducing a description (of some …

Stemming is a pre-processing step in Text Mining applications as well as a very common
requirement of Natural Language processing functions. In fact it is very important in most of …

Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English
corpora for topic modeling. In this work, we train and evaluate topic models on a variety of …

Background: During the last fifty years, improved information retrieval techniques have
become necessary because of the huge amount of information people have available, which …

Stemming is a program that matches the morphological variants of the word to its root word.
Stemming is extensively used as a pre-processing tool in the field of natural language …

Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval
(IR). Like IR, CLIR is centered on the search for documents and for information contained …

We propose a method to automatically train lemmatization rules that handle prefix, infix and
suffix changes to generate the lemma from the full form of a word. We explain how the …

In recent information explosion, contents in internet are multilingual and majority will be in
the form of natural languages. Processing of these natural languages for various language …

Derivational models are still an underresearched area in computational morphology. Even
for German, a rather resourcerich language, there is a lack of largecoverage derivational …