  In this paper, the problem of disambiguating a target word for Polish is
approached by searching for related words with known meaning. These relatives
are used to build a training corpus from unannotated text. This technique is
improved by proposing new rich sources of replacements that substitute the
traditional requirement of monosemy with heuristics based on wordnet relations.
The na\"ive Bayesian classifier has been modified to account for an unknown
distribution of senses. A corpus of 600 million web documents (594 billion
tokens), gathered by the NEKST search engine allows us to assess the
relationship between training set size and disambiguation accuracy. The
classifier is evaluated using both a wordnet baseline and a corpus with 17,314
manually annotated occurrences of 54 ambiguous words.
