This thesis deals with text processing applications examining methods suitable for less-resourced and agglutinative languages, thus presenting accurate preprocessing algorithms.

The first part of this study describes morphological tagging algorithms which can compute both the morpho-syntactic tags and lemmata of words accurately. A tool (called PurePos) was developed that was shown to produce precise annotations for Hungarian texts and also to serve as a good base for rule-based domain adaptation scenarios. Besides, we present a methodology for combining tagger systems raising the overall accuracy of Hungarian annotation systems.

Next, an application of the presented tagger is described that aims to produce morphological annotation for speech transcripts, and thus, the first morphological disambiguation tool for spoken Hungarian is introduced. Following this, a method is described which utilizes the adapted PurePos system for estimating morpho-syntactic complexity of Hungarian speech transcripts automatically.

The third part of the study deals with the preprocessing of electronic health records. On the one hand, a hybrid algorithm is presented for segmenting clinical texts into words and sentences accurately. On the other hand, domain-specific enhancements of PurePos are described showing that the resulting tagger has satisfactory performance on noisy medical records.

Finally, the main results of this study are summarized by presenting the author’s theses. Further on, applications of the methods presented are listed which aims less-resourced languages.

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