Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.21139.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:08:47.926823Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T12:35:29.932012Z
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 29bce1e8-8270-48b5-b097-f7f97eed8c09 · inbound
Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines cites this paper.
Advancing Arabic Reverse Dictionary Systems: A Transformer-Based Approach with Dataset Construction Guidelines Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:08:47.926823Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 06f065e8-4ffc-452e-8530-5d13f669928f · inbound
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training cites this paper.
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:35:29.975498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.