  There have been multiple attempts to resolve various inflection matching
problems in information retrieval. Stemming is a common approach to this end.
Among many techniques for stemming, statistical stemming has been shown to be
effective in a number of languages, particularly highly inflected languages. In
this paper we propose a method for finding affixes in different positions of a
word. Common statistical techniques heavily rely on string similarity in terms
of prefix and suffix matching. Since infixes are common in irregular/informal
inflections in morphologically complex texts, it is required to find infixes
for stemming. In this paper we propose a method whose aim is to find
statistical inflectional rules based on minimum edit distance table of word
pairs and the likelihoods of the rules in a language. These rules are used to
statistically stem words and can be used in different text mining tasks.
Experimental results on CLEF 2008 and CLEF 2009 English-Persian CLIR tasks
indicate that the proposed method significantly outperforms all the baselines
in terms of MAP.

Information Retrieval;Computation and Language