Paper: Using Decision Trees to Construct a Practical Parser

ACL ID C98-1080
Title Using Decision Trees to Construct a Practical Parser
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998
Authors

This l)al)er describes novel and practical .lal)anesc parsers that uses decision trees, l"irst, we COl> struct a single, decision tree to estimate modifica-- lion probabilities; how one phrase tends t.o modify another. Next, we introduce a boosting algorithm in which several decision t.rees are COllst.ructed and then combined for probalfility estiinat.ion. 'lThe two constructed parsers are evalua.ted I)y using the El)t{ .Japanese annotated corpus. The single-tree method outperforlns the conventional Japanese stochastic reel.hods by 4%. Moreover, the boosting version is shown to h;we significant adwmtages; 1 ) better pars- ing accuracy than ils single-tree counl.erparl for any alnoullt o[" training data and 2) no over-titling 1o data. for va.rious iterations.