Using Semantically Motivated Estimates to Help Subcategorization Acquisition

Using Semantically Motivated Estimates to Help Subcategorization Acquisition Anna Korhonen Computer Laboratory, University of Cambridge Pembroke Street, Cambridge CB2 3QG, UK alk23@cl, cam. ac. uk Abstract Research into the automatic acquisition of subcategorization frames from corpora is starting to produce large-scale computational lexicons which include valuable frequency in- formation. However, the accuracy of the resulting lexicons shows room for improve- ment. One source of error lies in the lack of accurate back-off estimates for subcatego- rization frames, delimiting the performance of statistical techniques frequently employed in verbal acquisition. In this paper, we propose a method of obtaining more accu- rate, semantically motivated back-off esti- mates, demonstrate how these estimates can be used to improve the learning of subcatego- rization frames, and discuss using the method to benefit large-scale lexical acquisition. 1 Introduction Manual development of large subcategorised lexicons has proved difficult because pred- icates change behaviour between sublan- guages, domains and over time. Yet parsers depend crucially on such information, and probabilistic parsers would greatly benefit from accurate information concerning the rel- ative frequency of different subcategorization frames (SCFs) for a given predicate. Over the past years acquiring subcatego- rization dictionaries from textual corpora has become increasingly popular (e.g. Brent, 1991, 1993; Ushioda et al., 1993; Briscoe and Carroll, 1997; Manning, 1993; Carroll and Rooth 1998; Gahl, 1998; Lapata, 1999, Sarkar and Zeman, 2000). The different approaches vary according to the methods used and the number of SCFs being extracted. Regardless of this, there is a ceiling on the performance of these systems at around 80% token recall*. *Token recall is the percentage of SCF tokens in a sample of manually analysed text that were correctly acquired by the system. One significant source of error lies in the statistical filtering methods frequently used to remove noise from automatically acquired SCFs. These methods are reported to be par- ticularly unreliable for low frequency scFs (Brent, 1991, 1993; Briscoe and Carroll, 1997; Manning, 1993; Manning and Schiitze, 1999; Korhonen, Gorrell and McCarthy, 2000), re- sulting in a poor overall performance. According to Korhonen, Gorrell and Mc- Carthy (2000), the poor performance of sta- tistical filtering can be largely explained by the zipfian nature of the data, coupled with the fact that many statistical tests are based on the assumption of two zipfian distributions correlating: the conditional SCF distribution of an individual verb (p(scfilverbj)) and the unconditional SCF distribution of all verbs in general (p(scfl)). Contrary to this assump- tion, however, there is no significant correla- tion between the two distributions. Korhonen, Gorrell and McCarthy (2000) have showed that a simple method of filtering SCFs on the basis of their relative frequency performs more accurately than statistical fil- tering. This method sensitive to the sparse data problem is best integrated with smooth- ing. Yet the performance of the sophisticated smoothing techniques which back-off to an un- conditional distribution also suffer from the lack of correlation between p(scfi[verbj) and p(scf0. In this paper, we propose a method for ob- taining more accurate back-off estimates …

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