Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models | Paper Citation Record

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This paper cites A guide to deep learning in healthcare.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A guide to deep learning in healthcare

Reference 1

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This paper cites Mining electronic health records: towards better research applications and clinical care.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Mining electronic health records: towards better research applications and clinical care

Reference 2

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This paper cites The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges

Reference 3

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This paper cites Mining Electronic Health Records (EHRs): A Survey.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Mining Electronic Health Records (EHRs): A Survey

Reference 4

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This paper cites Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care

Reference 5

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This paper cites An advanced review on text mining in medicine.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models An advanced review on text mining in medicine

Reference 6

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This paper cites Natural Language Processing for EHR-Based Computational Phenotyping.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Natural Language Processing for EHR-Based Computational Phenotyping

Reference 7

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This paper cites Clinical Text Data in Machine Learning: Systematic Review.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Clinical Text Data in Machine Learning: Systematic Review

Reference 8

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This paper cites Attention is all you need.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Attention is all you need

Reference 9

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This paper cites Transformers for Multi-label Classification of Medical Text: An Empirical Comparison.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Transformers for Multi-label Classification of Medical Text: An Empirical Comparison

Reference 10

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This paper cites Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Measurement of Semantic Textual Similarity in Clinical Texts: Comparison of Transformer-Based Models

Reference 11

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This paper cites Limitations of Transformers on Clinical Text Classification.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Limitations of Transformers on Clinical Text Classification

Reference 12

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This paper cites A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction

Reference 13

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This paper cites A Survey of Large Language Models.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Survey of Large Language Models

Reference 14

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This paper cites A Survey on Evaluation of Large Language Models.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Survey on Evaluation of Large Language Models

Reference 15

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 16

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This paper cites Large Language Models: A Survey.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Large Language Models: A Survey

Reference 17

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This paper cites Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine

Reference 18

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

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Reference 19

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This paper cites ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

Reference 20

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Reference 21

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This paper cites Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 22

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models SciBERT: A Pretrained Language Model for Scientific Text

Reference 23

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This paper cites MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval

Reference 24

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This paper cites A large language model for electronic health records.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A large language model for electronic health records

Reference 25

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models MIMIC-III, a freely accessible critical care database

Reference 26

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This paper cites Annotating longitudinal clinical narratives for de- identification: The 2014 i2b2/UTHealth corpus.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Annotating longitudinal clinical narratives for de- identification: The 2014 i2b2/UTHealth corpus

Reference 27

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This paper cites Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1

Reference 28

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This paper cites Overview and Importance of Data Quality for Machine Learning Tasks.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Overview and Importance of Data Quality for Machine Learning Tasks

Reference 29

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets

Reference 30

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data Validation for Machine Learning

Reference 31

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data Evaluation and Enhancement for Quality Improvement of Machine Learning

Reference 32

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This paper cites Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations

Reference 33

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data Readiness Report

Reference 34

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Executing Data Quality Projects Ten Steps to Quality Data and Trusted Information (TM)

Reference 35

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This paper cites A Short Review of the Literature on Automatic Data Quality.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Short Review of the Literature on Automatic Data Quality

Reference 36

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This paper cites A Practical Framework for Evaluating the Quality of Knowledge Graph.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Practical Framework for Evaluating the Quality of Knowledge Graph

Reference 37

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This paper cites Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use

Reference 38

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Defining and measuring completeness of electronic health records for secondary use

Reference 39

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Review: Electronic Health Records and the Reliability and Validity of Quality Measures: A Review of the Literature

Reference 40

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Quality Indicators for Text Data

Reference 41

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Outlier Detection for Improved Data Quality and Diversity in Dialog Systems

Reference 42

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This paper cites Aiming beyond the Obvious: Identifying Non- Obvious Cases in Semantic Similarity Datasets.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Aiming beyond the Obvious: Identifying Non- Obvious Cases in Semantic Similarity Datasets

Reference 43

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This paper cites Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

Reference 44

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss

Reference 45

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Confident Learning: Estimating Uncertainty in Dataset Labels

Reference 46

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This paper cites Detecting errors in part-of-speech annotation.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Detecting errors in part-of-speech annotation

Reference 47

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This paper cites Detecting errors within a corpus using anomaly detection.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Detecting errors within a corpus using anomaly detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.949138Z

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This paper cites Detecting annotation noise in automatically labelled data.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Detecting annotation noise in automatically labelled data

Reference 49

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Observation a4c4469b-22f9-4848-92a3-58315b77dea9 · outbound

This paper cites Data Quality for Machine Learning Tasks.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Data Quality for Machine Learning Tasks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.926965Z

Source-reported events for the cited work

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Observation f12bbf17-6848-46d2-8431-87a32a9590e4 · outbound

This paper cites Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

Reference 51

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Observation 25322a57-536d-472f-85fc-fa691ca1a474 · outbound

This paper cites Beyond Accuracy: Behavioral Testing of NLP Models with CheckList.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.904432Z

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This paper cites Impact of data quality for automatic issue classification using pre-trained language models.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Impact of data quality for automatic issue classification using pre-trained language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.894416Z

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 54

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 55

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Observation 26ad1efa-fb80-479f-bfa8-0c9ef9f67e88 · outbound

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 56

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This paper cites A Comprehensive Survey of Grammatical Error Correction.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models A Comprehensive Survey of Grammatical Error Correction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.540618Z

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Observation bf769fc2-7486-410f-a78e-6ba500cd2d87 · outbound

This paper cites The CoNLL-2014 Shared Task on Grammatical Error Correction.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models The CoNLL-2014 Shared Task on Grammatical Error Correction

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.519429Z

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Observation 6adabad0-8498-40c9-891d-ddb3f1aff8ee · outbound

This paper cites Grammatical Error Correction: A Survey of the State of the Art.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Grammatical Error Correction: A Survey of the State of the Art

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.505767Z

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Observation 7bc03968-b719-4430-ad1a-6ae77a1130c7 · outbound

This paper cites The Battle of LLMs: A Comparative Study in Conversational QA Tasks.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models The Battle of LLMs: A Comparative Study in Conversational QA Tasks

Reference 60

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This paper cites Beyond ChatGPT: Enhancing Software Quality Assurance Tasks with Diverse LLMs and Validation Techniques.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Beyond ChatGPT: Enhancing Software Quality Assurance Tasks with Diverse LLMs and Validation Techniques

Reference 61

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This paper cites Mixtral of Experts.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Mixtral of Experts

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T11:08:24.722196Z

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This paper cites Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.494327Z

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Observation bb4e021a-4630-45f6-a0d3-62f570be6037 · outbound

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T11:08:25.483346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5a2f893b-5f9f-40b0-87ce-633f23f9f552 · outbound

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Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Unresolved cited work

Reference 65

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This paper cites Distributed representations of words and phrases and their compositionality.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Distributed representations of words and phrases and their compositionality

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.473547Z

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Observation 4a7383fb-ca6a-408a-822d-656ac752d9ee · outbound

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

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.463566Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 150edee6-c040-4b05-86bb-7315896ed514 · outbound

This paper cites Publicly Available Clinical BERT Embeddings.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Publicly Available Clinical BERT Embeddings

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.452659Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9553200d-332b-48c1-82b3-ec60d4b58133 · outbound

This paper cites Longformer: The Long-Document Transformer.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Longformer: The Long-Document Transformer

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T11:08:24.838946Z

Source-reported events for the cited work

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Observation 120725a8-17ec-4b71-b19c-be2188833d25 · outbound

This paper cites Resident buzzed at2300hrs on5/10/10,her legs felt like they were burning n she was in pain.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Resident buzzed at2300hrs on5/10/10,her legs felt like they were burning n she was in pain

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.440910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99a3e58b-a47f-41ed-8f91-f78c40971625 · outbound

This paper cites Resident was sleeping on round check,repositioned by2 x staff fluids given. nil problems settled ator.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Resident was sleeping on round check,repositioned by2 x staff fluids given. nil problems settled ator

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.429295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 099afcfe-dfc1-4c95-8c6d-3e075e0da905 · outbound

This paper cites Resident.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Resident

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.271864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d51656ab-a47b-442d-b9d1-f368a6d055fc · outbound

This paper cites Resident.

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models Resident

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:08:25.207666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers