Unsupervised morpheme analysis evaluation by IR experiments -- Morpho Challenge 2007

This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are basic vocabulary units suitable for different tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant effect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods