Morpho Challenge evaluation by information retrieval
In Morpho Challenge competitions, the objective has been 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 (IR), and statistical language modeling. In this paper, we propose to evaluate the morpheme analyses by performing IR experiments, where the words in the documents and queries are replaced by their proposed morpheme representations and the search is based on morphemes instead of words. In this paper, the evaluations are run for three languages: Finnish, German, and English using the queries, texts, and relevance judgments available in CLEF forum. 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.