Paper Citation Record · LEDGER

Question Answering based Clinical Text Structuring Using Pre-trained Language Model

As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:1908.06606.

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

pith.paper-citation-record.v1
1908.06606 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:42:23.333635Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

26 of 26 outbound references displayed

verified exact0
verified fuzzy19
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1399c452-03af-4448-ba89-f899ed4f7460 · outbound

This paper cites Toward infor- mation extraction: identifying protein names from biological papers,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Toward infor- mation extraction: identifying protein names from biological papers,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:24.115909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78ffc820-23c0-4baf-848f-6c6f9d18d126 · outbound

This paper cites Linguistic mapping of terminologies to SNOMED CT,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Linguistic mapping of terminologies to SNOMED CT,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:24.095086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a487d800-ad97-44a3-a6ba-4fa871d823b3 · outbound

This paper cites Developing a hybrid dictionary- based bio-entity recognition technique,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Developing a hybrid dictionary- based bio-entity recognition technique,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:24.068777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da55bfd2-b6d2-425c-82e9-c5557082e1b9 · outbound

This paper cites Automated identiﬁcation of wound information in clinical notes of patients with heart diseases: Developing and validating a nat- ural language processing application,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Automated identiﬁcation of wound information in clinical notes of patients with heart diseases: Developing and validating a nat- ural language processing application,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:24.042750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1b94015e-349b-4b99-b291-18659226c9fb · outbound

This paper cites Development and validation of an au- tomated method for identifying patients undergoing radical cystectomy for bladder cancer using natural language processing,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Development and validation of an au- tomated method for identifying patients undergoing radical cystectomy for bladder cancer using natural language processing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:24.017861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4651f022-2c45-452e-a4fb-29486d180b63 · outbound

This paper cites Natural language processing for automated quantiﬁcation of brain metastases reported in free-text radiology reports,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Natural language processing for automated quantiﬁcation of brain metastases reported in free-text radiology reports,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.986639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ebb895e3-e149-4094-80fb-04b5b27cf182 · outbound

This paper cites Automated extraction of family history information from clinical notes,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Automated extraction of family history information from clinical notes,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.953361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 22d29a3d-63ec-417b-b539-1f363fdc4b75 · outbound

This paper cites ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.928621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4af39aa0-559a-4184-83d9-7d45d00f878e · outbound

This paper cites Fonferko-Shadrach, A.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Fonferko-Shadrach, A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.900298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2546361d-d5eb-4032-8a46-4c7a7d535a5a · outbound

This paper cites Improving language understanding by generative pre- training,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Improving language understanding by generative pre- training,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.873639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc73568f-c389-46fd-8fee-794ccb5fa6d1 · outbound

This paper cites Deep contextualized word representations,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Deep contextualized word representations,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.847044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3072e924-e133-46fd-9c73-06538d296553 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.188853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c2d0550-742f-44b3-b5ca-e7f9dfb3c064 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.207010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50c222c1-771a-408f-940d-0efeded3087b · outbound

This paper cites BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.215549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba21df03-cb8b-43f6-9715-0eccbb436f25 · outbound

This paper cites A neural named entity recognition and multi- type normalization tool for biomedical text mining,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model A neural named entity recognition and multi- type normalization tool for biomedical text mining,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.817696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a2d76d3-b109-471f-a09e-b75a21802ed6 · outbound

This paper cites Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random ﬁeld,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Chinese clinical named entity recognition using residual dilated convolutional neural network with conditional random ﬁeld,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.792137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3c8969b1-1302-4fad-8d20-c4b604930d3f · outbound

This paper cites Incorporating dictionaries into deep neural networks for the chinese clinical named entity recognition,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Incorporating dictionaries into deep neural networks for the chinese clinical named entity recognition,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.240685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ac91c54-f970-4a3a-903f-626e2882260c · outbound

This paper cites Bilinear cnn models for ﬁne- grained visual recognition,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Bilinear cnn models for ﬁne- grained visual recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.739038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bb4a9678-ccf9-4a50-8894-00c38e452979 · outbound

This paper cites Image style transfer using convolutional neural networks,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Image style transfer using convolutional neural networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.701694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ab0e770e-6559-4a5e-a097-85cf04598365 · outbound

This paper cites Boosted convolutional neural networks.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Boosted convolutional neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.676273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9bb4ba3b-e875-468f-8b71-120a14685491 · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model SQuAD: 100,000+ questions for machine comprehension of text,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.645618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ad621bf-87bc-472e-a136-880ff347ecbb · outbound

This paper cites A strategy on selecting performance metrics for classiﬁer evaluation,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model A strategy on selecting performance metrics for classiﬁer evaluation,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.293785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50896acc-0ef8-46c1-937c-8f35a7f387f4 · outbound

This paper cites Correlation analysis of performance metrics for classiﬁer,.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Correlation analysis of performance metrics for classiﬁer,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.590569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 99a88f5c-df43-47eb-bcd1-9d4f57db42e6 · outbound

This paper cites Chollet et al., “Keras,” https://keras.io, 2015.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Chollet et al., “Keras,” https://keras.io, 2015

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.312362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa75d087-9a57-4400-b7c1-385a8af776cb · outbound

This paper cites Tensorﬂow: a system for large- scale machine learning.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model Tensorﬂow: a system for large- scale machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:42:23.542778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 829b4f0c-4011-45bb-a79e-f1ca1379f639 · outbound

This paper cites QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension.

Question Answering based Clinical Text Structuring Using Pre-trained Language Model QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T12:42:23.333635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.