Tagging with Hidden Markov Models Using Ambiguous Tags

Part of speech taggers based on Hidden Markov Models rely on a series of hypotheses which make certain errors inevitable. The idea developed in this paper consists in allowing a limited, controlled ambiguity in the output of the tagger in order to avoid a number of errors. The ambiguity takes the form of ambiguous tags which denote subsets of the tagset. These tags are used when the tagger hesitates between the different components of the ambiguous tags. They are introduced in an existing lexicon and 3-gram database. Their lexical and syntactic counts are computed on the basis of the lexical and syntactic counts of their constituents, using impurity functions. The tagging process itself, based on the Viterbi algorithm, is unchanged. Experiments conducted on the Brown corpus show a recall of 0.982, for an ambiguity rate of 1.233 which is to be compared with a baseline recall of 0.978 for an ambiguity rate of 1.414 using the same ambiguous tags and with a recall of 0.955 corresponding to the one best solution of standard tagging (without ambiguous tags).