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• W3208588512 abstract "The millions of scientific papers published every year have made it well beyond any human’s ability to read and curate. As a result, valuable data and potentially ground-breaking insights remain buried deep in the mountain of publications. Unfortunately, while modern Information Extraction techniques show great promise, they are rarely developed for scientific text and often perform poorly in scientific scenarios. We present a Relation Extraction model that automates identification of relationships between entity pairs. We incorporate dependency parse information, relation-term attention, and specialized word embeddings in our model. Application to a benchmark dataset results in an F1 score of 0.755, outperforming the 0.682 by a more traditional model." @default.
• W3208588512 created "2021-11-08" @default.
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• W3208588512 date "2021-09-01" @default.
• W3208588512 modified "2023-09-26" @default.
• W3208588512 title "Gold Panning: Automatic Extraction of Scientific Information from Publications" @default.
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• W3208588512 doi "https://doi.org/10.1109/escience51609.2021.00042" @default.
• W3208588512 hasPublicationYear "2021" @default.
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