Automatic Disambiguation in the Corpora of Modern Greek and Yiddish
The problem of morphological ambiguity is widely addressed in the modern NLP. Mostly ambiguity is resolved with the use of large manually-annotated corpora and machine learning. However, such methods are not always available, as good training data is not accessible for all languages. In this paper we present a method of disambiguation without gold standard corpora using several statistical models, namely, Brill algorithm (Brill 1995) and unambiguous n-grams from the automatically annotated corpus. All the methods were tested on the Corpus of Modern Greek and on the Corpus of Modern Yiddish. As a result, more than a half of words with ambiguous analyses were disambiguated in both corpora, demonstrating high precision (>80%). Our method of morphological disambiguation demonstrates that it is possible to eliminate some of the ambiguous analyses in the corpus without specific linguistic resources, only with the use of raw data, where all possible morphological analyses for every word are indicated.