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 …

Tokenization is a fundamental step in processing textual data preceding the tasks of
information retrieval, text mining, and natural language processing. Tokenization is a …

Stemming is one of the pipeline feature of Information Retrieval and commonly used in
natural language processing and text mining. The main purpose of a stemming process is to …

The information world is rich of documents in different formats or applications, such as
databases, digital libraries, and the Web. Text classification is used for aiding search …

Stemming is one of the techniques in natural language processing that is used to reduce a
word to its root. Information retrieval and knowledge management can further be improved …

As the massive data is increasing exponentially on web and information retrieval systems
and the data retrieval has now become challenging. Stemming is used to produce …

Stemming is a process of reducing the inflected words to their root form. Stemming algorithm
for Malay language is very important especially in building an effective information retrieval …

Stemming is an operation that conflates morphologically similar terms into a single term
without doing complete morphological analysis. Stemming is used in information retrieval …

Stemming algorithms (stemmers) are used to convert the words to their root form (stem); this
process is used in the pre-processing stage of the Information Retrieval Systems. The …

There has been a massive growth in the volume of data produced around the world in last
few decades. The first data growing had begun about fifty years ago [1] when the …