Gully Burns


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Scientific Discourse Tagging for Evidence Extraction
Xiangci Li | Gully Burns | Nanyun Peng
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Evidence plays a crucial role in any biomedical research narrative, providing justification for some claims and refutation for others. We seek to build models of scientific argument using information extraction methods from full-text papers. We present the capability of automatically extracting text fragments from primary research papers that describe the evidence presented in that paper’s figures, which arguably provides the raw material of any scientific argument made within the paper. We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of “evidence fragments” (i.e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles. We first demonstrate our state-of-the-art scientific discourse tagger on two scientific discourse tagging datasets and its transferability to new datasets. We then show the benefit of leveraging scientific discourse tags for downstream tasks such as claim-extraction and evidence fragment detection. Our work demonstrates the potential of using evidence fragments derived from figure spans for improving the quality of scientific claims by cataloging, indexing and reusing evidence fragments as independent documents.


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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.


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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)


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The Role of Information Extraction in the Design of a Document Triage Application for Biocuration
Sandeep Pokkunuri | Cartic Ramakrishnan | Ellen Riloff | Eduard Hovy | Gully Burns
Proceedings of BioNLP 2011 Workshop


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Towards Automated Semantic Analysis on Biomedical Research Articles
Donghui Feng | Gully Burns | Jingbo Zhu | Eduard Hovy
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Adaptive Information Extraction for Complex Biomedical Tasks
Donghui Feng | Gully Burns | Eduard Hovy
Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing


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Extracting Data Records from Unstructured Biomedical Full Text
Donghui Feng | Gully Burns | Eduard Hovy
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)