Text mining for open domain semi-supervised Semantic Role Labeling

Quynh Ngoc Thi Do, Steven Bethard, Marie Francine Moens

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)33-48
Number of pages16
JournalCEUR Workshop Proceedings
StatePublished - 2014
Externally publishedYes
Event1st International Workshop on Data Mining and Natural Language Processing, DMNLP 2014 - Nancy, France
Duration: Sep 15 2014Sep 15 2014

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'Text mining for open domain semi-supervised Semantic Role Labeling'. Together they form a unique fingerprint.

Cite this