SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

@article{Augenstein2017SemEval2T,
  title={SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications},
  author={Isabelle Augenstein and Mrinal Das and S. Riedel and Lakshmi Vikraman and A. McCallum},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.02853}
}
We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and… Expand
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This study describes the design of the NTNU system for the ScienceIE task at the SemEval 2017 workshop. We use self-defined feature templates and multiple conditional random fields with extractedExpand
SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles
TLDR
The participating systems were evaluated by matching their extracted keyphrases against manually assigned ones and the overall ranking of the submitted systems is presented. Expand
PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge
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This paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications withsemble of unsupervised models, random forest and linear models used for candidate keyphrase ranking and keyphrase type classification. Expand
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
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This work presents a system based on a convolutional neural network to extract relations between scientific concepts and ranked first in the SemEval-2017 task 10 (ScienceIE) for relation extraction in scientific articles (subtask C). Expand
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This paper aimed at the creation of a keyphrase extraction approach which relies on as little external resources as possible, and without applying any hand-crafted external resources, and only utilizing a transformed version of word embeddings trained at Wikipedia. Expand
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TLDR
An end-to-end pipeline processing approach for SemEval 2017’s Task 10 to extract keyphrases and their relations from scientific publications by modeling the subtasks as sequential labeling and outperforms other techniques that do not employ global decoding and hence do not account for dependencies between keyphRases. Expand
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