- G. Tur, Kemal Oflazer. 1998. Tagging English by Path Voting Constraints. Abstract: We describe a constraint-based tagging approach where individual constraint rules vote on sequences of matching tokens and tags. Disambiguation of all tokens in a sentence is performed at the very end by selecting tags that appear on the path that receives the highest vote. This constraint application paradigm makes the outcome of the disambiguation independent of the rule sequence, and hence relieves the rule developer from worrying about potentially conflicting rule sequencing. The approach can also combine statistically and manually obtained constraints, and incorporate negative constraint rules to rule out certain patterns. We have applied this approach to tagging English text from the Wall Street Journal and the Brown Corpora. Our results from the Wall Street Journal Corpus indicate that with 4(}0 statistically derived constraint rules and about 800 hand-crafted constraint rules, we can attain an average accuracy of 97.89~ on the training corpus and an average accuracy of 97.50~o on the testing corpus. We can also relax the single tag per token limitation and allow ambiguous tagging which lets us trade recall and precision. 1 I n t r o d u c t i o n Part-of-speech tagging is one of the preliminary steps in many natural language processing systems in which the proper part-of-speech tag of the tokens comprising the sentences are disambiguated using either statistical or symbolic local contextual information. Tagging systems have used either a statistical approach where a large corpora is employed to train a probabilistic model which then is used to tag unseen text, (e.g., Church (1988), Cutting et al. (1992), DeR,ose (1988)), or a constraint-based approach which employs a large number of hand-crafted linguistic constraints that are used to eliminate impossible sequences or morphological parses for a given word in a given context, recently most prominently exemplified by the Constraint Grammar work (Karlsson et al., 1995; Voutilainen, 1995b; Voutilainen et al., 1992; Voutilainen and Tapanainen, 1993). Brill (1992; 1994; 1995) has presented a transformationbased learning approach. This paper extends a novel approach to constraint-based tagging first applied for Turkish (Oflazer and Tiir, 1997), which relieves the rule developer from worrying about contlicting rule ordering requirements and constraints. The approach depends on assigning votes to constraints via statistical and/or manual means, and then letting constraints vote on matching sequences on tokens, as depicted in Figure 1. This approach does not reflect the outcome of matching constraints to the set of morphological parses immediately as usually done in constraint-based systems. Only after all applicable rules are applied to a sentence, tokens are disambiguated in parallel. Thus, the outcome of the rule applications is independe,d of the order of rule applications. W1 W2 W3 W4 Wn Tokens tl tl .tl tl . . . tl R1 R3 R2 .-. Rm voting Rules Figure 1: Voting Constraint Rules
