FEASPAR : A Feature Structure Parser Learning to Parse Spoken Language

Abstract

We describe and experimentally evaluate a system, FeasPar, that learns parsing spontaneous speech. To train and run FeasPar (Feature Structure Parser), only limited handmodeled knowledge is required.The FeasPar architecture consists of neural networks and a search. The networks split the incoming sentence into chunks, which are labeled with feature values and chunk relations. Then, the search finds the most probable and consistent feature structure.FeasPar is trained, tested and evaluated with the Spontaneous Scheduling Task, and compared with a handmodeled LR-parser. The handmodeling effort for FeasPar is 2 weeks. The handmodeling effort for the LR-parser was 4 months. FeasPar performed better than the LR-parser in all six comparisons that are made.