The first algorithm PROMODES, which participated in the Morpho Challenge 2009 (an intema-tional competition for unsupervised morphological analysis ) employs a lower order model whereas the second algorithm PROMODES-H is a novel development of the first using a higher order model.

This study is called morphological analysis .

four tasks are assigned to morphological analysis : word decomposition into morphemes, building morpheme dictionaries, defining morphosyn-tactical rules which state how morphemes can be combined to valid words and defining mor-phophonological rules that specify phonological changes morphemes undergo when they are combined to words.

Results of morphological analysis are applied in speech synthesis (Sproat, 1996) and recognition (Hirsimaki et al., 2006), machine translation (Amtrup, 2003) and information retrieval (Kettunen, 2009).

We have presented two probabilistic generative models for word decomposition, PROMODES and PROMODES-H. Another generative model for morphological analysis has been described by Snover and Brent (2001) and Snover et al.

Combining different morphological analysers has been performed, for example, by Atwell and Roberts (2006) and Spiegler et al.

Morphisto, for example, generates alternative morphological analyses , so that the disambiguation algorithm performs a random choice between these.

(V) Integration with other ontological knowledge sources in order to improve the recall of morphosyntactic and morphological analyses (e.g., for disambiguating grammatical case).

These observations provide further support for our conclusion that the ontology-based integration of morphosyntactic analyses enhances both the robustness and the level of detail of morphosyntactic and morphological analyses .

2.2 Integrating different morphosyntactic and morphological analyses

(i) Morphisto, a morphological analyzer without contextual disambiguation (Zielinski and Simon, 2008),

In addition, morphological analysis plays a crucial role here, as highly frequent morpheme correspondences can be particularly revealing.

In addition, our model carries out an implicit morphological analysis of the lost language, utilizing the known morphological structure of the related language.

This interplay implicitly relies on a morphological analysis of words in the lost language, while utilizing knowledge of the known language’s lexicon and morphology.

rect morphological analysis of words in the lost language must be learned, we assume that the inventory and frequencies of prefixes and suffixes in the known language are given.

In summary, the observed input to the model consists of two elements: (i) a list of unanalyzed word types derived from a corpus in the lost language, and (ii) a morphologically analyzed lexicon in a known related language derived from a separate corpus, in our case nonparallel.

On the target side (Turkish), we only perform morphological analysis and disambiguation but treat the complete complex morphological tag as a factor, instead of separating morphemes.

On the Turkish side, we perform a full morphological analysis , (Oflazer, 1994), and morphological disambiguation (Yuret and Ture, 2006) to select the contextually salient interpretation of words.

6For example, the morphological analyzer outputs +A3 s g to mark a singular noun, if there is no explicit plural morpheme.

Goldwater and McClosky (2005) use morphological analysis on the Czech side to get improvements in Czech-to-English statistical machine translation.