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Word-level morphosyntactic descriptions, such as "Ncmsn" designating a common masculine singular noun in the nominative, have been developed for all Slavic languages, yet there have been few attempts to arrive at a proposal that would be harmonised across the languages. Standardisation adds to the interchange potential of the resources, making it easier to develop multilingual applications or to evaluate language technology tools across several languages. The process of the harmonisation of morphosyntactic categories, esp. for morphologically rich Slavic languages is also interesting from a language-typological perspective. The EU MULTEXT-East project developed corpora, lexica and tools for seven languages, with the focus being on morphosyntactic data, including formal, EAGLES-based specifications for lexical morphosyntactic descriptions. The specifications were later extended, so that they currently cover nine languages, five from the Slavic family: Bulgarian, Croatian, Czech, Serbian and Slovene. The paper presents these morphosyntactic specifications, giving their background and structure, including the encoding of the tables as TEI feature structures. The five Slavic language specifications are discussed in more depth.

This paper presents a process of building a Sentiment Analysis Framework for Serbian (SAFOS). We created a hybrid method that uses a sentiment lexicon and Serbian WordNet (SWN) synsets assigned with sentiment polarity scores in the process of feature selection. As the use of stemming for morphologically rich languages (MRLs) may result in loss or giving incorrect sentiment meaning to words, we decided to expand the sentiment lexicon, as well as the lexicon generated using SWN, by adding morphological forms of emotional terms and phrases. It was done using Serbian Morphological Electronic Dictionaries. A new feature reduction method for document-level sentiment polarity classification using maximum entropy modeling is proposed. It is based on mapping of a large number of related feature candidates (sentiment words, phrases and their inflectional forms) to a few concepts and using them as features. Testing was performed on a 10-fold cross validation set and on test sets containing news and movie reviews. The results of all experiments show that sentiment feature mapping for feature set reduction achieves better results over the basic set of features. For both test sets, the best classification accuracy scores were achieved for the combination of unigram and bigram features reduced by sentiment feature mapping (accuracy 78.3 % for movie reviews and 79.2 % for news test set). In 10-fold cross-validation, best average accuracy score of 95.6 % was obtained using unigrams as features, reduced by the mapping procedure.

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