Paper Citation Record · LEDGER

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models

As of 22 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2501.08974.

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

pith.paper-citation-record.v1
2501.08974 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:16:22.299123Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy5
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66e0df8b-f153-4cf6-81c5-c8fa44b1cfbd · outbound

This paper cites T., & Szolovits, P.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models T., & Szolovits, P

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:22.504152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f71914f2-efd0-40da-ba4c-8fd6e35abe01 · outbound

This paper cites (2007, June).

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models (2007, June)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:22.488259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 839646ba-ed70-49b5-9260-4f703699b735 · outbound

This paper cites Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:22.253840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 291a1713-0c14-41cc-9758-0cc3f18b6627 · outbound

This paper cites an unresolved cited work.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:16:22.471277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 38f131de-7049-4378-bc0d-fb22ff19ff60 · outbound

This paper cites & Lempitsky, V.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models & Lempitsky, V

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:22.456484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 354d8a15-5ab2-4b3e-9798-7e2543c3e1a5 · outbound

This paper cites Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:22.269445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ea0bbef-01bd-4614-9fce-f39659c6899f · outbound

This paper cites B., & Guestrin, C.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models B., & Guestrin, C

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:22.440859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f2073799-802a-4f17-85a3-4908efca9a49 · outbound

This paper cites an unresolved cited work.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:16:22.426215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 55128722-c094-4149-9383-a3feac2578c4 · outbound

This paper cites Nearest Neighbor Machine Translation.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Nearest Neighbor Machine Translation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:22.284448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4253b01-de7a-41d9-ab40-bb8c96c41929 · outbound

This paper cites & Krishnan, D.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models & Krishnan, D

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:16:22.410935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 013878c4-70cc-401b-81aa-3dab6877acc6 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:22.294057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66cec82-d66a-4dd2-84d2-37442d127094 · outbound

This paper cites Aspect Level Sentiment Classification with Deep Memory Network.

Learning to Extract Cross-Domain Aspects and Understanding Sentiments Using Large Language Models Aspect Level Sentiment Classification with Deep Memory Network

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T20:16:22.299123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.