Unsupervised Morpheme Analysis
Evaluation by IR experiments at Morpho Challenge 2007
Mikko Kurimo, Mathias Creutz and Ville Turunen
In: Morpho Challenge Workshop at CLEF 2007, 19-21 Sep 2007, Budapest, Hungary.
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.
The challenge was part of the EU Network of Excellence PASCAL Challenge Program
and organized in collaboration with CLEF.