Paper Citation Record · LEDGER

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore

As of 19 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2505.14165.

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

pith.paper-citation-record.v1
2505.14165 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:39.387845Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

16 of 16 outbound references displayed

verified exact1
verified fuzzy13
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b0f217d7-9d07-4608-96bd-0cc72d76b841 · outbound

This paper cites Aspect based fine-grained sentiment analysis for online reviews.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Aspect based fine-grained sentiment analysis for online reviews

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.109331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 260f076e-2ed1-4468-ac38-2c92ff606abc · outbound

This paper cites Comprehensive analysis of aspect term extraction methods using various text embeddings.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Comprehensive analysis of aspect term extraction methods using various text embeddings

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.944017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87108129-a313-4d21-b990-16a8e4ee44fe · outbound

This paper cites A survey on aspect-based sentiment classification.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore A survey on aspect-based sentiment classification

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.807793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ecc6b6af-8944-4b9b-b5de-f0268347be06 · outbound

This paper cites Ceg: A joint model for causal commonsense events enhanced story ending generation.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Ceg: A joint model for causal commonsense events enhanced story ending generation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.486317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d38962d7-1dd5-4e9e-8e2b-6e4d268ccbae · outbound

This paper cites Prompt-based learning for aspect-level sentiment classification.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Prompt-based learning for aspect-level sentiment classification

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.260738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4cc82f52-bb42-4c40-9b16-583a8f4e53af · outbound

This paper cites Semeval-2016 task 5: Aspect based sentiment analysis.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Semeval-2016 task 5: Aspect based sentiment analysis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.077242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d15dde19-1ea7-4c7c-92aa-6343de5399f6 · outbound

This paper cites Syntax-aware graph attention network for aspect-level sentiment classification.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Syntax-aware graph attention network for aspect-level sentiment classification

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.864016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0ffb82b7-d8b5-4958-8a55-848963a225ee · outbound

This paper cites A unified model for opinion target extraction and target sentiment prediction.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore A unified model for opinion target extraction and target sentiment prediction

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.657575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a5918cab-de24-4b86-8d7a-13a7a754f878 · outbound

This paper cites The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore The biases of pre-trained language models: An empirical study on prompt-based sentiment analysis and emotion detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.434699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 96b9ea31-28f3-49fb-ab1f-a90b353d5728 · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:38.828658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c5397c4-b5f4-4a19-a694-c783d3e803f7 · outbound

This paper cites Harnessing domain insights: A prompt knowledge tuning method for aspect-based sentiment analysis.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Harnessing domain insights: A prompt knowledge tuning method for aspect-based sentiment analysis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.243924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73d7d7f3-f346-4e53-b8ee-98d653820e58 · outbound

This paper cites Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:38.992877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19cb3fbb-d51f-490e-9463-ce9e8f27ebbb · outbound

This paper cites Causalabsc: Causal inference for aspect debiasing in aspect-based sentiment classification.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Causalabsc: Causal inference for aspect debiasing in aspect-based sentiment classification

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.129148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08ac7285-c9b9-4a18-b1e5-adc3b405106c · outbound

This paper cites Study on mindspore deep learning framework.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Study on mindspore deep learning framework

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.969354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 17c64405-a28f-4bf9-972d-943cd88b5aab · outbound

This paper cites Few-shot Hate Speech Detection Based on the MindSpore Framework.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Few-shot Hate Speech Detection Based on the MindSpore Framework

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:42:39.577377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22110519-3884-4a10-a635-994afe59c062 · outbound

This paper cites Aspect based sentiment analysis semeval-2014 task 4.

PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore Aspect based sentiment analysis semeval-2014 task 4

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.769033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.