Jonathan Gordon

Postdoctoral researcher, USC Information Sciences Institute

About

My research is on artificial intelligence: I study how to automatically learn commonsense knowledge from large-scale text and how reasoning with this knowledge supports natural language understanding.

At ISI, I’m working with Jerry Hobbs. My doctoral advisor was Lenhart Schubert.

Publications and Presentations

Structured Generation of Technical Reading Lists.

Jonathan Gordon, Stephen Aguilar, Emily Sheng, and Gully Burns.

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 2017.

An Investigation into the Pedagogical Features of Documents.

Jonathan Gordon, Stephen Aguilar, Emily Sheng, and Gully Burns.

Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA), 2017.

BD2K ERuDIte: the Educational Resource Discovery Index for Data Science.

José Luis Ambite, Lily Fierro, Florian Geigl, Jonathan Gordon, Gully A. P. C. Burns, Kristina Lerman, and John D. Van Horn.

Proceedings of the Fourth WWW Workshop on Big Scholarly Data: Towards the Web of Scholars (BigScholar), 2017.

Distribution and Inference.

Jerry R. Hobbs and Jonathan Gordon.

Presented at the ESSLLI Workshop on Distributional Semantics and Linguistic Theory (DSALT), 2016.

Modeling Concept Dependencies in a Scientific Corpus.

Jonathan Gordon, Linhong Zhu, Aram Galstyan, Prem Natarajan, and Gully A. P. C. Burns.

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 2016.

A Corpus of Rich Metaphor Annotation.

Jonathan Gordon, Jerry R. Hobbs, Jonathan May, Michael Mohler, Fabrizio Morbini, Bryan Rink, Marc Tomlinson, and Suzanne Wertheim.

Proceedings of the Third Workshop on Metaphor in NLP, 2015.

High-Precision Abductive Mapping of Multilingual Metaphors.

Jonathan Gordon, Jerry R. Hobbs, Jonathan May, and Fabrizio Morbini.

Proceedings of the Third Workshop on Metaphor in NLP, 2015.

Inferential Commonsense Knowledge from Text.

Jonathan Gordon.

PhD Thesis, University of Rochester, 2014.

Reporting Bias and Knowledge Acquisition.

Jonathan Gordon and Benjamin Van Durme.

Proceedings of the CIKM Workshop on Automated Knowledge Base Construction (AKBC), 2013. (Best Paper Award)

WordNet Hierarchy Axiomatization and the Mass–Count Distinction.

Jonathan Gordon and Lenhart Schubert.

Proceedings of the IEEE International Conference on Semantic Computing (ICSC), 2013.

Using Textual Patterns to Learn Expected Event Frequencies.

Jonathan Gordon and Lenhart Schubert.

Proceedings of the NAACL Workshop on Automatic Knowledge Base Construction and Web-Scale Knowledge Extraction (AKBC-WEKEX), 2012.

Towards Adequate Knowledge and Natural Inference.

Lenhart Schubert, Jonathan Gordon, Karl Stratos, and Adina Rubinoff.

Proceedings of the AAAI Fall Symposium on Advances in Cognitive Systems, 2011.

Episodic Logic: Natural Logic + Reasoning.

Karl Stratos, Lenhart Schubert, and Jonathan Gordon.

Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD), 2011.

Discovering Commonsense Entailment Rules Implicit in Sentences.

Jonathan Gordon and Lenhart Schubert.

Proceedings of the EMNLP Workshop on Textual Entailment (TextInfer), 2011.

Quantificational Sharpening of Commonsense Knowledge.

Jonathan Gordon and Lenhart Schubert.

Proceedings of the AAAI Fall Symposium on Commonsense Knowledge, 2010.

Learning from the Web: Extracting General World Knowledge from Noisy Text.

Jonathan Gordon, Benjamin Van Durme, and Lenhart Schubert.

Proceedings of the AAAI Workshop on Collaboratively-built Knowledge Sources and Artificial Intelligence (WikiAI), 2010.

Evaluation of Commonsense Knowledge with Mechanical Turk.

Jonathan Gordon, Benjamin Van Durme, and Lenhart Schubert.

Proceedings of the NAACL Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, 2010.

Weblogs as a Source for Extracting General World Knowledge.

Jonathan Gordon, Benjamin Van Durme, and Lenhart Schubert.

Proceedings of the Fifth International Conference on Knowledge Capture (K-CAP), 2009.