Paper Citation Record · LEDGER

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2604.22325.

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

pith.paper-citation-record.v1
2604.22325 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T12:04:41.518077Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-08T12:04:41.518077Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-11T19:21:09.598495Z

Reference resolution

19 of 19 outbound references displayed

verified exact2
verified fuzzy9
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b77bb9bf-8e38-43e4-b25b-e38926703daf · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:42:24.602937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be5946a7-4bab-4365-a669-c432036ac46a · outbound

This paper cites It handles the entire process through a novel text acquisi- tion method that leverages web retrieval and LLM-based generation to produce descriptive text for classifier training.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks It handles the entire process through a novel text acquisi- tion method that leverages web retrieval and LLM-based generation to produce descriptive text for classifier training

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.621966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c4c7cb8-8536-4ee6-8d2a-770ff2b9d12c · outbound

This paper cites Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-11T19:21:09.601648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61159a61-4f8e-4718-8b5e-ad1735e52772 · outbound

This paper cites Evaluation results indicate that our framework achieves robust performance across domains.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Evaluation results indicate that our framework achieves robust performance across domains

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.615630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b680ffab-e83d-481a-a2f9-9487cceb775f · outbound

This paper cites In contrast, our research aims to acquire knowledge about real-world entities irrespective of any specific mention or document.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks In contrast, our research aims to acquire knowledge about real-world entities irrespective of any specific mention or document

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.612122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff45dbf4-76e3-4bff-ba43-e7254b78aedf · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:42:24.630966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c6faa25-69ef-4b06-b70e-fb9a63b26501 · outbound

This paper cites task-relevant.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks task-relevant

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.636871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f84f450-efe7-4aee-b818-2f4dfa96643f · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:21:09.607752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca40974f-2889-488b-be8f-be858a4ddbd2 · outbound

This paper cites I don’t have current detailed in- formation.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks I don’t have current detailed in- formation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.643376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9fe39de-96e5-4b9c-a1e6-ea81aa1473ef · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:42:24.608520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 312acf63-04a0-4aef-8d3e-bb9d2fcacd8d · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:42:24.606004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25090bcc-f4a4-4d28-aa19-6c0a1aa01181 · outbound

This paper cites We thank Tianyu Jiang for helpful clarifications on their publicly released codebase, which facilitated our reproduction of the results reported in their work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks We thank Tianyu Jiang for helpful clarifications on their publicly released codebase, which facilitated our reproduction of the results reported in their work

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.639948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b4e6fe64-c407-4c57-89d4-6a839b7d0dd3 · outbound

This paper cites an unresolved cited work.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-05-26T13:42:24.633701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c2bd1d97-8c6c-472f-9a9e-11916696a8f5 · outbound

This paper cites InProceedings of the 6th International The Se- mantic Web and 2nd Asian Conference on Asian Semantic Web Conference, ISWC’07/ASWC’07, page 722–735, Berlin, Heidelberg.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks InProceedings of the 6th International The Se- mantic Web and 2nd Asian Conference on Asian Semantic Web Conference, ISWC’07/ASWC’07, page 722–735, Berlin, Heidelberg

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.600112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 80f10dc8-6ceb-444a-807b-043843768fe9 · outbound

This paper cites Longformer: The Long-Document Transformer.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Longformer: The Long-Document Transformer

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T19:21:09.594638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d29d8635-2f00-461a-a866-84db921c4a07 · outbound

This paper cites Internet-Augmented Dialogue Generation.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Internet-Augmented Dialogue Generation

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:21:09.587816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57f1b7c6-7314-492e-9313-6948bd265244 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-05-11T19:21:09.591265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 972fb10f-5322-49a2-bbee-efa8b1cfab3d · outbound

This paper cites InProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 715–725, Lisbon, Portugal.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks InProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 715–725, Lisbon, Portugal

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.628256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c973c16e-4661-4383-8f70-dc4a06834a34 · outbound

This paper cites Louise and Hollan- der, Allan D.

Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks Louise and Hollan- der, Allan D

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:42:24.624954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 2c4c7cb8-8536-4ee6-8d2a-770ff2b9d12c · inbound