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Paper Citation Record · LEDGER

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering

As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2509.00990.

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

pith.paper-citation-record.v1
2509.00990 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:04:04.003748Z

measured 21 of 21 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

21 of 21 outbound references displayed

  • verified exact4
  • verified fuzzy3
  • unresolved11
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 800b27c7-05ca-48d0-834e-9c9c51b33b4a · outbound

This paper cites Top2Vec: Distributed Representations of Topics.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Top2Vec: Distributed Representations of Topics

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.936235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.936235Z digest=sha256:07c507ca2870441f76863397fd5984b87dc311446ce21bdb709d766402a4b240

Observation db152ef0-1ef9-4dff-885e-7de3f2bb144a · outbound

This paper cites Ariai and G.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Ariai and G

Reference 2

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unresolved
no resolver link, observed 2026-08-05T13:04:03.941114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.941114Z digest=sha256:9e9c7c96fe5b174b836ee9ac13aabeeb2e30f83143374be067c62314faf65c78

Observation a4e39c9b-e191-4008-bb2d-24c44069ce06 · outbound

This paper cites Methods for Computing Legal Document Similarity: A Comparative Study.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Methods for Computing Legal Document Similarity: A Comparative Study

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:04:04.957410Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.944312Z digest=sha256:acb3bd1e0d8af303c8609b8e9b68f94fea471fc41b35ea139351183f8523c7f4

Observation db25309e-7ae4-4104-bbe6-97ac4669468b · outbound

This paper cites Legal case document similarity: You need both network and text.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Legal case document similarity: You need both network and text

Reference 4

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:04:04.794395Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.947938Z digest=sha256:61727f173254029b5ec18becc1d81d34075aaedeaa5d2ec276b4f0c7a3903b2b

Observation 0fec29ee-2c52-4e25-a8f6-0b5f75345366 · outbound

This paper cites Latent dirichlet allocation.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Latent dirichlet allocation

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:04:05.526293Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.951734Z digest=sha256:8b7271bd394856d6a89dacf9778700fcb8c34ecfb99375c57c4f7724866b5786

Observation 022f7919-a6a0-4e69-a9d8-223033d52035 · outbound

This paper cites Large-scale multi-label text classification on EU legislation.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Large-scale multi-label text classification on EU legislation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.955796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.955796Z digest=sha256:61714fd107a6296994b2a1423559085361d43ea57a118cfa5466b19792a816ad

Observation 96ac50de-b662-4f9c-ac0b-18edfcfff7d9 · outbound

This paper cites LEGAL-BERT: The Muppets straight out of Law School.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering LEGAL-BERT: The Muppets straight out of Law School

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.959236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.959236Z digest=sha256:e3f158ae1374006e48e50b703ff805e019765378cc6ff7b87da822c20afb871b

Observation 76c17033-f497-4efc-a020-5981ef9057db · outbound

This paper cites Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:04:04.623768Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.962387Z digest=sha256:c4277bd0f7d882a6686149c1fa72dbe0bde167ddecc5c5383a9bc2453ac23669

Observation a416c185-72da-41d3-a613-45f8073c3e49 · outbound

This paper cites node2vec: Scalable feature learning for networks.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering node2vec: Scalable feature learning for networks

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.965649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.965649Z digest=sha256:33a5ecd5d000873578f346d6d64f88f269d6a73982f61ae949b1f8f3107f9d09

Observation 7e67aee3-d581-4b1e-98a5-038b273bf973 · outbound

This paper cites CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.968363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.968363Z digest=sha256:c3df66b21d9d316368a9eef6a8de2c118af4767feafcbab63c986df146c137f5

Observation e0ba78b9-a343-4efb-ae08-abf1482092f4 · outbound

This paper cites Learning the parts of objects by non-negative matrix factorization.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Learning the parts of objects by non-negative matrix factorization

Reference 11

Resolution
malformed identifier
no resolver link, observed 2026-08-05T13:04:03.971898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.971898Z digest=sha256:a8a9ff0b6d2a467a37b501c926127ffb6ad0c6c52e54b53ea7b045864f4bef70

Observation 993477fc-3c92-4167-8f1f-3ded834a8c93 · outbound

This paper cites Exploratory analysis of legal case citation data using node embedding.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Exploratory analysis of legal case citation data using node embedding

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:04:05.281371Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.975937Z digest=sha256:39d3f378dbfe840920d9a48c01b58898d70d35e3f2ec257612d15bf3cf59b797

Observation bf7ecd5b-158a-4d58-ba64-9344a8b2f62e · outbound

This paper cites MacQueen.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering MacQueen

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.979148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.979148Z digest=sha256:9fbeedb781b5e3143ce9b2e95010cf09347372dc8b04f540d78972404f5ad673

Observation 94658417-cb6f-44c5-ba47-0b42b0ad6183 · outbound

This paper cites Normalized Mutual Information to evaluate overlapping community finding algorithms.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Normalized Mutual Information to evaluate overlapping community finding algorithms

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.982130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.982130Z digest=sha256:4297581f8f0ebb8c280e8f08735f7044beb465fd43de22bfeee66eb95d303985

Observation 620e8625-15cb-40d6-aeaf-0e8bc0103484 · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.985063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.985063Z digest=sha256:041a7cef8675ab44628683b7e165dc717419645cedaca66910630cf8bacb9c59

Observation b68283c1-3e71-4c48-a84c-047c2e6e3bb7 · outbound

This paper cites An analysis of topic modelling for legislative texts.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering An analysis of topic modelling for legislative texts

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:04:05.153413Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.988064Z digest=sha256:f7edd13674294ec87a1d4a73f705aea154f5569a39a0dc9d215012a70765a7bc

Observation 09d00316-e6e3-44c2-8379-ea03397f752b · outbound

This paper cites Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.991350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.991350Z digest=sha256:bd1a07256e541068dcb62426243c663adf0c409d1dc127885a99461597d2a886

Observation dd6dc8ea-c373-48ec-bf19-8d0dd2b28398 · outbound

This paper cites Estimating the dimension of a model.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Estimating the dimension of a model

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-05T13:04:03.994302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:04:03.994302Z digest=sha256:2292dcb5e7afad526324571a8e3fe87c4ab6f5327f8bf0e3c8265478d461faca

Observation afba0faa-4928-470a-8fc8-61b90caa55ec · outbound

This paper cites Email thread identification using latent dirichlet allocation and non-negative matrix factorization based clustering techniques.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Email thread identification using latent dirichlet allocation and non-negative matrix factorization based clustering techniques

Reference 19

Resolution
verified exact
doi, observed 2026-08-05T13:04:04.031912Z

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.

source=arxiv_source observed=2026-08-05T13:04:03.997741Z digest=sha256:78302f66487dc4f88ac9cfcb5547e2789d53101632b525ad6d61254841c9a017

Observation 7bd16cee-8e2a-454d-baa9-ce7d5018f521 · outbound

This paper cites Acord: An expert-annotated retrieval dataset for legal contract drafting.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering Acord: An expert-annotated retrieval dataset for legal contract drafting

Reference 20

Resolution
verified exact
raw_fallback, observed 2026-08-05T13:04:04.255157Z

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.

source=arxiv_source observed=2026-08-05T13:04:04.000830Z digest=sha256:c291ce373cdbfd33ba0439d1e9457e727149c7c646d77cc5aff3ed04ccc06297

Observation 2de0b51e-b325-45cd-9f6d-52bd968cddf5 · outbound

This paper cites When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings.

Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings

Reference 21

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T13:04:04.175444Z

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.

source=arxiv_source observed=2026-08-05T13:04:04.003748Z digest=sha256:8db98f8497182e14d2af6bb90491b2fac81ae3980eaaa0736a23cce56962dda9

Pith citing papers

No inbound Pith citation observations are available.