  There are two main methodologies for constructing the knowledge base of a
natural language analyser: the linguistic and the data-driven. Recent
state-of-the-art part-of-speech taggers are based on the data-driven approach.
Because of the known feasibility of the linguistic rule-based approach at
related levels of description, the success of the data-driven approach in
part-of-speech analysis may appear surprising. In this paper, a case is made
for the syntactic nature of part-of-speech tagging. A new tagger of English
that uses only linguistic distributional rules is outlined and empirically
evaluated. Tested against a benchmark corpus of 38,000 words of previously
unseen text, this syntax-based system reaches an accuracy of above 99%.
Compared to the 95-97% accuracy of its best competitors, this result suggests
the feasibility of the linguistic approach also in part-of-speech analysis.
