The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction

By Bridger Waleed Ammar, Matthew E. Peters, Chandra Bhagavatula, Russell Power. 2017

61 citations recorded by the source corpus.

This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Semi-supervised sequence tagging with bidirectional language models (2017, background): “Ammar et al., 2017).”
Construction of the Literature Graph in Semantic Scholar (2018, unknown): “See Peters et al. (2017) and Ammar et al. (2017) for more details.”
A dataset and baselines for sequential open-domain question answering (2018, methodology): “embedding model itself by adopting ideas from the relation extraction literature (Miwa and Bansal, 2016; Peng et al., 2017; Ammar et al., 2017).”
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (2018, methodology): “Using these datasets, neural models (Ammar et al., 2017, 2018; Luan et al., 2017b; Augenstein and Søgaard, 2017) are introduced for extracting scientiﬁc information.”