Zeljko Agic

Investigating language independence in HMM PoS/MSD-tagging

The paper presents an investigation of functional dependencies in morphosyntactic tagging using hidden Markov models. Starting from a well known fact that the HMM tagging paradigm relies on lexical knowledge acquired from training corpora and stored in form of transition and emission matrices, also called a language model, in the experiment, we apply the TnT trigram tagger on creating language models for seven different languages from MULTEXT East version 3 project translations of George Orwellpsilas novel 1984. - Czech, Estonian, Hungarian, Romanian, Serbian, Slovene and original English version. We then use these language models in the tagging procedure and obtain details on various relations between training corpora statistics, training outputs and outputs of the tagging procedure.

Error analysis in Croatian morphosyntactic tagging

In this paper, we provide detailed insight on properties of errors generated by a stochastic morphosyntactic tagger assigning Multext-East morphosyntactic descriptions to Croatian texts. Tagging the Croatia Weekly newspaper corpus by the CroTag tagger in stochastic mode revealed that approximately 85 percent of all tagging errors occur on nouns, adjectives, pronouns and verbs. Moreover, approximately 50 percent of these are shown to be incorrect assignments of case values. We provide various other distributional properties of errors in assigning morphosyntactic descriptions for these and other parts of speech. On the basis of these properties, we propose rule-based and stochastic strategies which could be integrated in the tagging module, creating a hybrid procedure in order to raise overall tagging accuracy for Croatian.