Distributed Representations for Unsupervised Semantic Role Labeling

Kristian Woodsend, Mirella Lapata

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


We present a new approach for unsupervised semantic role labeling that leverages distributed representations. We induce embeddings to represent a predicate, its arguments and their complex interdependence. Argument embeddings are learned from surrounding contexts involving the predicate and neighboring arguments, while predicate embeddings are learned from argument contexts. The induced representations are clustered into roles using a linear programming formulation of hierarchical clustering, where we can model task-specific knowledge. Experiments show improved performance over previous unsupervised semantic role labeling approaches and other distributed word representation models.
Original languageEnglish
Title of host publicationProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Print) 978-1-941643-32-7
Publication statusPublished - 2015
EventConference on Empirical Methods in Natural Language Processing - Lisbon, Portugal
Duration: 17 Sep 201521 Sep 2015


ConferenceConference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2015
Internet address

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