The effect of alternative tree representations on tree bank grammars

/ / I I / I I I l / The effect of alternative tree representations on tree bank grammars Mark Johnson* Cognitive and Linguistic Sciences, Box 1978 Brown University Providence, RI 02912, USA Mark_Johnson@Brown. edu Abstract The performance of PCFGs estimated from tree banks is shown to be sensitive to the par- ticular way in which linguistic constructions are represented as trees in the tree bank. This paper presents a theoretical analysis of the effect of different tree representations for PP attachment on PCFG models, and introduces a new methodology for empirically examin- ing such effects using tree transformations. It shows that one transformation, which copies the label of a parent node onto the labels of its children, can improve the performance of a PCFG model in terms of labelled precision and recall on held out data from 73% (pre- cision) and 69% (recall) to 80% and 79% re- spectively. It also points out that if only max- imum likelihood parses are of interest then many productions can be ignored, since they are subsumed by combinations of other pro- ductions in the grammar. In the Penn II tree bank grammar, almost 9% of productions are subsumed in this way. 1 Introduction Parsers which are capable of analysing unre- stricted text are of considerable scientific inter- est, and have technological applications in ar- eas such as machine translation and informa- tion retrieval as well. One way to produce such a parser is to extract a grammar from one of the larger tree bank corpora currently available. The relative frequency estimator described below provides a simple way to estimate from a tree bank corpus a Probabalistic Context Free Grammar (PCFG) that generates Part Of I would like to thank Chris Manning, whose observa- tion that PCFG parsers do not accurately reproduce PP attachment preferences in their training data stimulated this work, as well as Eugene Charniak, Stuart Geman and our students at Brown. Speech (POS) tags. Such a PCFG induced from a sufficiently large corpus typically generates all possible POS tag strings. A parsing system can be obtained by using a parser to find the max- imum likelihood parse tree for an input string. Such parsing systems often perform as well as other broad coverage parsing systems for pre- dicting tree structure from POS tags (Char- niak, 1996). In addition, many more sophis- ticated parsing models are elaborations of such PCFG models, so understanding the properties of PCFGs is likely to be useful (Charniak, 1997; Collins, 1997). It is well-known that natural language ex- hibits dependencies that Context Free Gram- mars (CFGs), and hence PCFGs, cannot de- scribe (Shieber, 1985). But as explained be- low, the independence assumptions implicit in PCFGs introduce biases in the statistical model induced from a tree bank even in constructions which are adequately described by a CFG. The direction and size of these biases depend on fac- tors such as the following: • the precise tree structures used in the tree bank, and • whether the …

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