No abstract

Purpose -To show that stem generation compares well with lemmatization as a morphological tool for a highly inflectional language for IR purposes in a best-match retrieval system. Design/methodology/approach -Effects of three different morphological methodslemmatization, stemming and stem production -for Finnish are compared in a probabilistic IR environment (INQUERY). Evaluation is done using a four-point relevance scale which is partitioned differently in different test settings. Findings -Results show that stem production, a lighter method than morphological lemmatization, compares well with lemmatization in a best-match IR environment. Differences in performance between stem production and lemmatization are small and they are not statistically significant in most of the tested settings. It is also shown that hitherto a rather neglected method of morphological processing for Finnish, stemming, performs reasonably well although the stemmer used -a Porter stemmer implementation -is far from optimal for a morphologically complex language like Finnish. In another series of tests, the effects of compound splitting and derivational expansion of queries are tested. Practical implications -Usefulness of morphological lemmatization and stem generation for IR purposes can be estimated with many factors. On the average P-R level they seem to behave very close to each other in a probabilistic IR system. Thus, the choice of the used method with highly inflectional languages needs to be estimated along other dimensions too. Originality/value -Results are achieved using Finnish as an example of a highly inflectional language. The results are of interest for anyone who is interested in processing of morphological variation of a highly inflected language for IR purposes.

No abstract

In this article, we are studying the differences between the European Union languages using statistical and unsupervised methods. The analysis is conducted in the different levels of language: the lexical, morphological and syntactic. Our premise is that the difficulty of the translation could be perceived as differences or similarities in different levels of language. The results are compared to linguistic groupings. Two approaches are selected for the analysis. A Kolmogorov complexity based approach is used to compare the language structure in syntactic and morphological levels. A morpheme-level comparison is conducted based on an automated segmentation of the languages into morpheme-like units. The way the languages convey information in these levels is taken as a measure of similarity or dissimilarity between languages and the results are compared to classical linguistic classifications. The results have a significant impact on the design of (statistical) machine translation systems. If the source language conveys information in the morphological level and the target language in the syntactic level, it is clear that the machine translation system must be able to transfer the information from one level to another.

No abstract