Jonathan Gordon


2019

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Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects
James Mullenbach | Jonathan Gordon | Nanyun Peng | Jonathan May
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine’s captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question “Given a whole described by an adjective, does the adjective also describe a given part?” We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects

2017

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An Investigation into the Pedagogical Features of Documents
Emily Sheng | Prem Natarajan | Jonathan Gordon | Gully Burns
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the “pedagogical value” of the document to the learner. While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of “pedagogical roles” of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.

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Structured Generation of Technical Reading Lists
Jonathan Gordon | Stephen Aguilar | Emily Sheng | Gully Burns
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Learners need to find suitable documents to read and prioritize them in an appropriate order. We present a method of automatically generating reading lists, selecting documents based on their pedagogical value to the learner and ordering them using the structure of concepts in the domain. Resulting reading lists related to computational linguistics were evaluated by advanced learners and judged to be near the quality of those generated by domain experts. We provide an open-source implementation of our method to enable future work on reading list generation.

2016

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Modeling Concept Dependencies in a Scientific Corpus
Jonathan Gordon | Linhong Zhu | Aram Galstyan | Prem Natarajan | Gully Burns
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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High-Precision Abductive Mapping of Multilingual Metaphors
Jonathan Gordon | Jerry Hobbs | Jonathan May | Fabrizio Morbini
Proceedings of the Third Workshop on Metaphor in NLP

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A Corpus of Rich Metaphor Annotation
Jonathan Gordon | Jerry Hobbs | Jonathan May | Michael Mohler | Fabrizio Morbini | Bryan Rink | Marc Tomlinson | Suzanne Wertheim
Proceedings of the Third Workshop on Metaphor in NLP

2012

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Using Textual Patterns to Learn Expected Event Frequencies
Jonathan Gordon | Lenhart Schubert
Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction (AKBC-WEKEX)

2011

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Discovering Commonsense Entailment Rules Implicit in Sentences
Jonathan Gordon | Lenhart Schubert
Proceedings of the TextInfer 2011 Workshop on Textual Entailment

2010

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Evaluation of Commonsense Knowledge with Mechanical Turk
Jonathan Gordon | Benjamin Van Durme | Lenhart Schubert
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk