Abstract
In this paper, a supervised learning method of semantic role labeling is presented. It is based on maximum entropy conditional probability models. This method acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed for a few words selected from sections of the Wall Street Journal part of the Penn Treebank corpus. © Springer-Verlag Berlin Heidelberg 2004.
Cite
CITATION STYLE
Moreda, P., Fernández, M., Palomar, M., & Suárez, A. (2004). Identifying semantic roles using maximum entropy models. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3206, pp. 163–170). Springer Verlag. https://doi.org/10.1007/978-3-540-30120-2_21
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
