Recognising Clauses Using Symbolic and Machine Learning Approaches
Benjamin Clayton Hachey · 2002
Clauses are important for a variety of NLP tasks such as predicting phrasing in text-tospeech synthesis and inferring text alignment for machine translation (Ejerhed 1988, Leffa 1998, Papageorgiou 1997). The Computational Natural Language Learning 2001 shared task (Sang & Dejean 2001) set the goal of identifying clause boundaries in text using machine learning methods. Systems created for the task predicted a label for each word specifying the number of clauses starting and ending at that position in the sentence without differentiating between clause types. This work extends that of the shared task in several ways: (1) performance bounds are explored, (2) an attempt is made to distinguish ‘main’ and ‘subordinate’ clauses, and (3) Winnow and maximum entropy, model classes proven effective in similar domains yet not previously employed for the task, are applied to the problem.