Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective

Ivan Titov, Alexandre Klementiev

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking into ways to improve supervised SRL systems by producing surrogate annotated data and reducing sparsity of lexical features or (2) considering completely unsupervised semantic role induction settings. In this work, we aim to link these two veins of research by studying how unsupervised techniques can be improved by exploiting small amounts of labeled data. We extend a state-of-the-art Bayesian model for unsupervised semantic role induction to better accommodate for annotated sentences. Our semi-supervised method outperforms a strong supervised baseline when only a small amount of labeled data is available.
Original languageEnglish
Title of host publicationCOLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 8-15 December 2012, Mumbai, India
PublisherAssociation for Computational Linguistics
Number of pages18
Publication statusPublished - 2012


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