Probabilistic and Rule-Based Tagger of an Inflective Language- a Comparison

Probabilistic and Rule-Based Tagger of an Inflective Language- a Comparison Jan Haji~ and Barbora Hladk~i Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Malostransk~ n£m. 25 CZ-118 00 Prague 1 e-maih (hajic,hladka} ~ufal.mff.cuni.cz Abstract We present results of probabilistic tag- ging of Czech texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective lan- guages. After description of the tag system used, we show the results of four experi- ments using a simple probabilistic model to tag Czech texts (unigram, two bigram experiments, and a trigram one). For com- parison, we have applied the same code and settings to tag an English text (another four experiments) using the same size of training and test data in the experiments in order to avoid any doubt concerning the va- lidity of the comparison. The experiments use the source channel model and maxi- mum likelihood training on a Czech hand- tagged corpus and on tagged Wall Street Journal (WSJ) from the LDC collection. The experiments show (not surprisingly) that the more training data, the better is the success rate. The results also indicate that for inflective languages with 1000+ tags we have to develop a more sophisti- cated approach in order to get closer to an acceptable error rate. In order to compare two different approaches to text tagging -- statistical and rule-based -- we modified Eric Brill's rule-based part of speech tag- ger and carried out two more experiments on the Czech data, obtaining similar results in terms of the error rate. We have also run three more experiments with greatly reduced tagset to get another comparison based on similar tagset size. 1 INTRODUCTION Languages with rich inflection like Czech pose a special problem for morphological disambiguation (which is usually called tagging1). For example, the ending "-u" is not only highly ambiguous, but at the same time it carries complex information: it corre- sponds to the genitive, the dative and the locative singular for inanimate nouns, or the dative singu- lar for animate nouns, or the accusative singular for feminine nouns, or the first person singular present tense active participle for certain verbs. There are two different techniques for text tagging: a stochas- tic technique and a rule-based technique. Each ap- proach has some advantages -- for stochastic tech- niques there exists a good theoretical framework, probabilities provide a straightforward way how to disambiguate tags for each word and probabilities can be acquired automatically from the data; for rule-based techniques the set of meaningful rules is automatically acquired and there exists an easy way how to find and implement improvements of the tag- ger. Small set of rules can be used, in contrast to the large statistical tables. Given the success of statis- tical methods in different areas, including text tag- ging, given the very positive results of English statis- tical taggers and given the fact that there existed no statistical tagger for any Slavic language we wanted to apply statistical methods …

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