Feaspar: a feature structure parser learning to parse spoken language

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 neu-
ral networks and a search. The networks spilt 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 Schednling 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.