Semantic role labeling has become a popular natural language processing task in recent years. A number of conferences have addressed this task for the English language and many different approaches have been applied to the task. In particular, some have used a memory-based learning approach. This thesis further develops the memory-based learning approach to semantic role labeling through the use of analogical modeling of language. Data for this task were taken from a previous conference (CoNLL-2005) so that a direct comparison could be made with other algorithms that attempted to solve this task. It will be shown here that the current approach is able to closely compare to other memory-based learning systems on the same task. Future work is also addressed.
College and Department
Humanities; Linguistics and English Language
BYU ScholarsArchive Citation
Casbeer, Warren C., "Semantic Role Labeling with Analogical Modeling" (2008). Theses and Dissertations. 1475.
semantic role labeling, memory-based processing, analogical modeling