Publication Type |
poster |
School or College |
Scientific Computing and Imaging Institute |
Department |
Computing, School of |
Creator |
Gilbert, Nathan Alan; Riloff, Ellen M. |
Title |
Exploring Knowledge-Rich Solutions to Noun Phrase Coreference Resolution |
Description |
Coreference resolution is the task of identifying coreferent expressions in text. Accurate coreference resolution can improve other tasks such as machine translation, information retrievel and document summarization. Currently, the best approaches involve some form of supervised Machine Learning algorithms, which requires annotated corpora. This requirement is expensive and time consuming. The initial stage of this project was to implement a state of the art supervised learning based coreference system. The general flow of this system is presented next, notice that for supervised learning systems, the classifier must be trained on annotated text. |
Type |
Text; Image |
Publisher |
University of Utah |
Language |
eng |
Bibliographic Citation |
Gilbert, N, & Riloff, E. (2010). Exploring Knowledge-Rich Solutions to Noun Phrase Coreference Resolution. University of Utah. |
Rights Management |
(c) Nathan Gilbert & Ellen Riloff |
Format Medium |
application/pdf |
Format Extent |
209,344 bytes |
Identifier |
ir-main/14795 |
ARK |
ark:/87278/s68k7tsr |
Setname |
ir_uspace |
ID |
707598 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s68k7tsr |