Paper: FeasPar - A Feature Structure Parser Learning To Parse Spoken Language

ACL ID C96-1033
Title FeasPar - A Feature Structure Parser Learning To Parse Spoken Language
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1996
Authors

We describe and experimentally evalu- ate a system, FeasPar, that learns pars- ing 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 Fea- sPar is 2 weeks. The handmodeling ef- fort for the LR-parser was 4 months. FeasPar performed better than the LR- parser in all six comparisons that are made.