This paper discusses an on-going project aiming at improving the quality and the efficiency of a rule-based parser by the addition of a statistical component. The proposed technique relies on bigrams of pairs word+category selected from the homographs contained in our lexical database and computed over a large section of the Hansard corpus, previously tagged. The bigram table is used by the parser to rank and prune the set of alternatives. To evaluate the gains obtained by the hybrid system, we conducted two manual evaluations. One over a small subset of the Hansard corpus, the other one with a corpus of about 50 articles taken from the magazine  The Economist . In both cases, we compare analyses obtained by the parser with and without the statistical component, focusing only on one important source of mistakes, the confusion between nominal and verbal readings for ambiguous words such as  announce ,  sets ,  costs ,  labour , etc.
['Eric Wehrli', 'Luka Nerima']
When Rules Meet Bigrams