Paper Citation Record · LEDGER

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1703.02507.

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

pith.paper-citation-record.v1
1703.02507 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:55:08.293702Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T14:46:50.743346Z

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 267fbd51-5ab9-42a3-84cc-e775565f3260 · inbound

Hamming Sentence Embeddings for Information Retrieval cites this paper.

Hamming Sentence Embeddings for Information Retrieval Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T13:13:56.956162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 821fd209-3bf3-4e4f-bbfa-7933e567af31 · inbound

How Sequence-to-Sequence Models Perceive Language Styles? cites this paper.

How Sequence-to-Sequence Models Perceive Language Styles? Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T13:05:24.017622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ae3c87b-92e5-4bdc-85c3-ee103104e031 · inbound

BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched EMR cites this paper.

BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched EMR Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-11T14:46:50.749257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9a6abff5-8a9a-4952-865a-3cded4797614 · inbound

When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey cites this paper.

When Deep Learning Meets Information Retrieval-based Bug Localization: A Survey Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Reference 110

Resolution: unresolved; no resolver link, observed 2026-08-16T04:55:08.293702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.