Text mining for open domain semi-supervised Semantic Role Labeling

The identification and classification of some circumstance semantic roles like Location, Time, Manner and Direction, a task of Semantic Role Labeling (SRL), plays a very important role in building text understanding applications. However, the performance of the current SRL systems on those roles is often very poor, especially when the systems are applied on domains other than the ones they are trained on. We present a method to build open domain SRL system, in which the training data is expanded by replacing its predicates by words in the testing domain. A language model, which is considered as a text mining technique, and some linguistic resources are used to select from the vocabulary of the testing domain the best words for the replacement. We apply our method on the case study of transferring a semantic role labeler trained on the news domain to the children story domain. It gives us valuable improvements over the four circumstance semantic roles Location, Time, Manner and Direction.