Developing a Multilingual Stemmer for the Requirement of Text Categorization and Information Retrieval

Information retrieval IR is the process of finding information (generally documents) that matches the needs of the user. One way to improve the search effectiveness, as well as the quality of text categorization is to build an effective stemmer that helps to match users' queries with relevant documents in IR and reduce the space of textual representation in TC. This has been always an interesting research topic in IR and TC. We can define stemming as the process of reducing inflected and derived words to their reduced forms (stems or roots). Many stemmers have been developed for different languages, but there is always many weaknesses and problems. In the present work, we have developed a multilingual stemming approach, based on the extraction of the word root and that exploits the technique of n-grams of characters. Our experiments have been done on three languages which are: Arabic, English, and French.