Statistical Filtering and Subcategorization Frame Acquisition

Statistical Filtering and Subcategorization Frame Acquisition Anna Korhonen and Genevieve Gorrell Computer Laboratory, University of Cambridge Pembroke Street, Cambridge CB2 3QG, UK alk23@cl, cam. ac. uk, genevieve, gorrell@netdecisions, co. uk Diana McCarthy School of Cognitive and Computing Sciences University of Sussex, Brighton, BN1 9QH, UK dianam@cogs, susx. ac. uk Abstract Research "into the automatic acquisition of subcategorization frames (SCFS) from corpora is starting to produce large-scale computa- tional lexicons which include valuable fre- quency information. However, the accuracy of the resulting lexicons shows room for im- provement. One significant source of error lies in the statistical filtering used by some re- searchers to remove noise from automatically acquired subcategorization frames. In this pa- per, we compare three different approaches to filtering out spurious hypotheses. Two hy- pothesis tests perform poorly, compared to filtering frames on the basis of relative fre- quency. We discuss reasons for this and con- sider directions for future research. 1 Introduction Subcategorization information is vital for suc- cessful parsing, however, manual develop- ment of large subcategorized lexicons has proved difficult because predicates change be- haviour between sublanguages, domains and over time. Additionally, manually devel- oped sucategorization lexicons do not provide the relative frequency of different SCFs for a given predicate, essential in a probabilistic ap- proach. Over the past years acquiring subcatego- rization dictionaries from textual corpora has become increasingly popular. The different approaches (e.g. Brent, !991, 1993; Ushioda et al., 1993; Briscoe and Carroll, 1997; Man- ning, 1993; Carroll and Rooth, 1998; Gahl, 1998; Lapata, 1999; Sarkar and Zeman, 2000) vary largely according to the methods used and the number of SCFS being extracted. Re- gardless of this, there is a ceiling on the perfor- mance of these systems at around 80% token recall 1 zWhere token recall is the percentage .of SCF to- kens in a sample of manually analysed text that were The approaches to extracting SCF informa- tion from corpora have frequently employed statistical methods for filtering (e.g. Brent, 1993; Manning 1993; Briscoe and Carroll, 1997; Lapata, 1999). This has been done to remove the noise that arises when dealing with naturally occurring data, and from mistakes made by the SCF acquisition system, for ex- ample, parser errors. Filtering is usually done with a hypothe- sis test, and frequently with a variation of the binomial filter introduced by Brent (1991, 1993). Hypothesis testing is performed by for- mulating a null hypothesis, (H0), which is as- sumed true unless there is evidence to the con- trary. If there is evidence to the contrary, H0 is rejected and the alternative hypothe- sis (H1) is accepted. In SCF acquisition, H0 is that there is no association between a particu- lar verb (verbj) and a SCF (SCFi), meanwhile H1 is that there is such an association. For SCF acquisition, the test is one-tailed since H1 states the direction of the association, a pos- itive correlation between verbj and scfi. We compare the expected probability of scfi oc- curring with verbj if H0 is true, to the …

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