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PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted…

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Blended score 0.398 — every figure below is computed, none of it is editorial.
FactorWeightScore ContributionWhere it came from
Corroboration 0.35 0.39 +0.135 34% 1 independent org on the story. Tier-3 aggregators never corroborate — they can show something is circulating, never that it is true.
Source trustleads 0.25 0.85 +0.212 53% arXiv is the highest-trust source on this story and is first-party — the organisation announcing its own news. Trust is taken from the best source, not averaged.
Pickup rate 0.20 0.00 +0.000 0% One counted organisation, so there is no spread to measure — nothing has picked this up to set a rate.
Freshness 0.20 0.25 +0.050 13% Halves every 10 hours from the newest item on the story. This is the only factor that rewards a story for nothing more than being recent.

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Read the full article at Arxiv →

What happened

Medical relation extraction (MRE) is commonly known for extracting entities and their relations jointly from a medical text, which has attracted considerable attention in recent years. Previous studies treat MRE as a sequence tagging task, which results in either a challenging design of the tagging schema or a failed extraction of multiple relations, due to intricate relationships among medical entities. In this work, we review the task from the linguistic perspective and propose a novel pipeline framework, PiPMRE, developed on language models to enhance MRE performance. Specifically, PiPMRE consists of a relation generator and a relation filter.

1independent orgs
40story score
0velocity
85source trust
4passes seen

How this story arrived

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  1. 01 Arxivfirst-party first seen PiPMRE: A Pipeline Based on Language Model for Medical Relation Extraction

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