This is a hybrid system: rules, neural network, rules. First
rules for the sure cases are applied, then a neural network
disambiguator is applied, then rules for repairing of the most
frequent errors of the neural network. The rules are implemented
as constraints in CLaRK System. The neural network is additional
module implemented in Java. It is called CLaRK. It requires the
morphologically annotated input.
Written, synchronic, general, manually annotated; 50 000 tokens, 2600 sentences extracted from the BulTreeBank Text Archive in order to contain the most frequent ambiguity classes in Bulgarian
The tokenizer is covering all languages that use Latin1, Laitn2, Latin3 and Cyrillic tables of Unicode. Can be extended to cover other tables in Unicode if necessary. The implementation is as a cascaded regular grammar in CLaRK. It recognizes over 60 token categories. It is easy to be adapted to new token categories.
Statistical analysis service: It calculates P(cue|class): probability of seeing a linguistic cue given a lexical class. This probability is computed given the occurrences of cues in a corpus (codified in the signatures file) and the information of belonging or not belonging of these words to different classes (codified in indicators file).
The probability is computed for each studied cue in the signatures file and for each class in the indicators file.
Angabe von orthographischen, morphologischen (Wortformenbildung und Wortbildung) sowie semantischen Informationen (Synonymie; Hyperonymie/Hyponymie); Zuordnung der Wörter zu der jeweiligen syntaktischen Kategorie (bei Substantiven zusätzlich Angabe des Genus)