An Unsupervised Model for Statistically Determining Coordinate Phrase Attachment

An Unsupervised Model for Statistically Determining Coordinate Phrase Attachment Miriam Goldberg Central High School & Dept. of Computer and Information Science 200 South 33rd Street Philadelphia, PA 19104-6389 University of Pennsylvania miriamgOunagi, cis. upenn, edu Abstract This paper examines the use of an unsuper- vised statistical model for determining the at- tachment of ambiguous coordinate phrases (CP) of the form nl p n2 cc n3. The model pre- sented here is based on JAR98], an unsupervised model for determining prepositional phrase at- tachment. After training on unannotated 1988 Wall Street Journal text, the model performs at 72% accuracy on a development set from sections 14 through 19 of the WSJ TreeBank [MSM93]. 1 Introduction The coordinate phrase (CP) is a source of struc- tural ambiguity in natural language. For exam- ple, take the phrase: box of chocolates and roses 'Roses' attaches either high to 'box' or low to 'chocolates'. In this case, attachment is high, yielding: H-attach: ((box (of chocolates)) (and roses)) Consider, then, the phrase: salad of lettuce and tomatoes 'Lettuce' attaches low to 'tomatoes', giving: L-attach: (salad (of ((lettuce) and (tomatoes))) [AR98] models. In addition to these, a corpus- based model for PP-attachment [SN97] has been reported that uses information from a semantic dictionary. Sparse data can be a major concern in corpus- based disambiguation. Supervised models are limited by the amount of annotated data avail- able for training. Such a model is useful only for languages in which annotated corpora are available. Because an unsupervised model does not rely on such corpora it may be modified for use in multiple languages as in [AR98]. The unsupervised model presented here trains from an unannotated version of the 1988 Wall Street Journal. After tagging and chunk- ing the text, a rough heuristic is then employed to pick out training examples. This results in a training set that is less accurate, but much larger, than currently existing annotated cor- pora. It is the goal, then, of unsupervised train- ing data to be abundant in order to offset its noisiness. 2 Background The statistical model must determine the prob- ability of a given CP attaching either high (H) or low (L), p( attachment I phrase). Results shown come from a development corpus of 500 phrases of extracted head word tuples from the WSJ TreeBank [MSM93]. 64% of these phrases attach low and 36% attach high. After further development, final testing will be done on a sep- arate corpus. The phrase: Previous work has used corpus-based ap- proaches to solve the similar problem of prepo- sitional phrase attachment. These have in- cluded backed-off [CB 95], maximum entropy [RRR94], rule-based [HR94], and unsupervised (busloads (of ((executives) and (their wives))) gives the 6-tuple: L busloads of executives and wives 610 where, a = L, nl = busloads, p = of, n2 = executives, cc = and, n3 = wives. The CP at- tachment model must determine a for all (nl p n2 cc n3) sets. The attachment decision is correct if it is the …

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