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Paper Citation Record · LEDGER

Attention Guided Graph Convolutional Networks for Relation Extraction

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.07510.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1906.07510 v8

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:55:30.563418Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T05:56:11.424693Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation efcff6d3-7aaa-4313-802c-8ffbf7074bd7 · inbound

A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction cites this paper.

A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction Attention Guided Graph Convolutional Networks for Relation Extraction

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T00:55:30.563418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:55:30.563418Z digest=sha256:5c223e8eb37c0193a2201d315d19e3922d847d5fa23c21436f13f1069f057f78

Observation 97e67648-fe60-42d4-a6eb-ae3f3411c490 · inbound

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval cites this paper.

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval Attention Guided Graph Convolutional Networks for Relation Extraction

Reference 277

Resolution
verified exact
arxiv_id, observed 2026-05-10T05:56:11.425935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T05:43:04.813867Z digest=sha256:809819c2d3552b7ba49f0cc33ee14c0b30e0b85da80468795d1c21ed94e938a0