Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)

Ibne Farabi Shihab; Jonathan Wood; Sudesh Bhagat

REVIEW 4 major objections 6 minor 14 references

Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read ALBERT, a compact transformer, outperformed SVM, XGBoost, and BERT models at identifying misclassified intersection-related crash narratives, reaching 73% agreement with the lead expert on disputed cases.

desk verdict A well-intentioned crash-narrative study whose central ALBERT superiority claim rests on a biased 100-case expert sample and several unsupported headline numbers. read the letter →

arxiv 2507.03066 v1 pith:OOCUTVSW submitted 2025-07-03 cs.CL cs.AI

Sudesh Bhagat , Ibne Farabi Shihab , Jonathan Wood This is my paper · ORCID

classification cs.CLcs.AI

keywords MisclassificationCrashnarrativesIntersection-relatedcrashesNaturallanguageprocessingALBERTExpertvalidationMachinelearningDeep

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that automated text classification of police crash narratives can reliably find intersection-related crashes that were mislabeled in a state database. Using 2019 Iowa crash data, the authors compare SVM, XGBoost, BERT, and ALBERT models and measure each against independent expert readings of 100 disputed narratives. They claim ALBERT achieves the highest agreement with expert classifications (73% with Expert 1) and that its performance is statistically indistinguishable from human inter-expert consistency. If true, agencies could use ALBERT as a screening tool to flag likely misclassifications for targeted manual review, improving crash data quality at scale.

What carries the argument

ALBERT (A Lite BERT), a transformer with cross-layer parameter sharing that keeps the model small while retaining contextual language understanding, is fine-tuned on bigram TF-IDF features from police crash narratives to classify each crash as intersection-related or not. The validation machinery is expert review: four domain experts independently classify 100 narratives where at least one model disagreed with the tabular data, and agreement percentages plus Cohen's $\kappa$ measure whether a model's judgments match human judgment.

What would settle it

Re-run the expert review on a random sample of crash narratives drawn from the full 2019 Iowa dataset rather than only cases where models disagreed with the tabular data; if ALBERT no longer shows the highest agreement with the experts (or if the differences among models vanish), the central claim that ALBERT is the best misclassification detector is not supported.

Watch

Extended reading notes

Core claim

The central discovery is that a parameter-efficient transformer, ALBERT (A Lite BERT), trained on crash narratives identifies potentially misclassified intersection-related crashes more reliably than traditional machine learning (SVM, XGBoost) or other BERT-based deep learning models. On a validation set of 100 narratives where model classifications disagreed with the tabular data, ALBERT agreed with Expert 1 on 73% of cases and with the original tabular labels on 58%, while also showing moderate-to-strong Cohen's $\kappa$ agreement (0.33-0.46) with all experts. The paper further reports that adding structured crash data through a hybrid multi-modal approach raises accuracy to 92.4%, a 54.2% error reduction relative to narrative-only ALBERT. The authors conclude that a hybrid workflow—automated screening by ALBERT plus targeted expert review—offers a practical path to correcting misclassified crash records.

Load-bearing premise

The whole ranking of models rests on the assumption that the 100 narratives chosen for expert review—picked because at least one model disagreed with the tabular label—are representative enough of all misclassified narratives that agreement with experts on this disputed subset is a fair way to decide which model finds real misclassifications.

Editorial extensions

If this is right

If ALBERT's expert-level agreement holds, transportation agencies can screen all crash narratives automatically and prioritize for manual review only the records ALBERT flags, cutting the volume of human review.
The hybrid multi-modal approach suggests that combining structured fields (geospatial coordinates, traffic control device presence) with narrative text yields higher classification accuracy than text alone.
Because ALBERT's advantage is concentrated on ambiguous narratives (proximity references, conflicting location indicators), it is most useful where crash records are hardest to code.
Agencies adopting this workflow could reduce misallocation of safety funds and improve network screening, since fewer high-risk intersections would be overlooked.
The finding that BERT variants make more proximity-related errors than traditional ML models indicates architecture choice matters for narrative classification.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural extension the paper does not test is whether the ALBERT screening threshold can be tuned to trade false positives against expert-review workload; the 73% agreement suggests many flagged cases would still be confirmed as correct, so operational cost depends on the chosen confidence threshold.
The same validation design could be transferred to other frequently misclassified variables—work-zone involvement, distraction, or restraint use—where narrative cues may behave differently than intersection language.
Because experts themselves reach only moderate kappa (0.27-0.50), the paper implicitly sets a ceiling on any single-model 'true' accuracy estimate; agreement with one expert may understate ALBERT's real performance if expert labels are noisy.
The 100-narrative sample drawn from model-disagreement cases likely overstates difficulty; a random sample of all records would probably show higher agreement between models and tabular data, so the practical screening benefit could be larger than the headline accuracies suggest.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First thing to know: the paper's main claim—that ALBERT beats other models at identifying misclassified intersection crashes—doesn't survive contact with its own evaluation design. The expert validation set was built by selecting cases where at least one model disagreed with the original label, so it is exactly the disagreement-enriched subset on which model rankings are most unstable. On top of that, several headline numbers (92.4% multimodal accuracy, 54.2% error reduction, Minnesota generalizability, 47.3% mitigation improvement) appear without any methodological support.

What is genuinely useful: the paper tackles a real data-quality problem that matters for safety analysis, and it does something uncommon—it brings in four blinded domain experts to review narratives. The inter-rater agreement results on the 100 cases (experts at 27-50% Cohen's kappa) are honest evidence that this classification task is genuinely hard. That's a useful data point for the field. The error analysis in Section 3.3, though not reproducible, gives a sensible taxonomy of why narratives get misclassified (proximity references, implicit intersection terms, etc.). The recommendation to use automated screening followed by targeted expert review is reasonable as a practical idea.

Now the soft spots. The selection-bias problem is load-bearing, not minor. Because the 100 cases were chosen by model disagreement, the agreement percentages in Tables 6-8 are conditional statements about a biased subset. Table 6 itself shows the problem: ALBERT's agreement with the original label drops from 88.17% on the full test set to 58% on the subset, so the subset is clearly not representative. The models are trained on those same original labels, so 'accuracy' in Tables 4 and 5 is just training fidelity, not correctness. The paper actually acknowledges the sample is 'disagreement-enriched' in Section 2.5, but then proceeds to use it as a benchmark anyway.

The unsupported numbers are a second independent problem. Section 3.6 claims a 92.4% accuracy and 54.2% error reduction for the hybrid approach, but no details are given on how the multimodal features were built, what the test setup was, or where the numbers come from. Similarly, the Minnesota validation dataset appears out of nowhere in Section 3.2.7 with no description of data, processing, or sample size. The count inconsistency between the 2000-narrative sample and the thousands of 'potentially misclassified' crashes in Table 3 also undermines confidence.

For whom is this paper? A reader who wants to see where crash-narrative NLP currently stands might find the model comparison useful as a rough orientation, but nobody should treat the numerical results as reliable. The paper deserves a serious referee because the topic is important and the authors show good faith—they just need to redesign the evaluation with a random sample and report all experimental details. My recommendation: send it to review with a major-revision invitation, but make clear the central ranking claims are not supported without a representative expert sample and proper reporting of the multimodal and generalizability analyses.

Referee Report

4 major / 6 minor

Summary. The paper trains SVM, XGBoost, BERT Sentence Embeddings, BERT Word Embeddings, and ALBERT on 2019 Iowa crash narratives to identify intersection-related crashes that may be misclassified in the tabular road-type field. The authors select 100 narratives on which at least one model disagrees with the tabular label, have four domain experts independently classify those narratives, and report agreement percentages and Cohen's/Fleiss' kappa statistics. They claim that ALBERT has the highest agreement with expert classifications (73% with Expert 1) and with original tabular labels (58% on the disputed sample), and that a hybrid multimodal approach reaches 92.4% accuracy with a 54.2% error reduction over the narrative-only ALBERT model. On this basis they recommend a two-stage screening pipeline combining ALBERT with targeted expert review.

Significance. The underlying question—whether narrative text can help correct misclassified intersection-related crash records—is practically important for transportation safety management, and the paper has several strengths: a broad and clearly described model set, explicit hyperparameter reporting, blinded expert review, systematic inter-rater reliability analysis, and a useful qualitative error taxonomy. However, the headline claims are not supported by the study as designed. The expert validation sample is selected using the very models being compared, and several of the most consequential numbers (the 92.4% multimodal accuracy, the 54.2% error reduction, the 47.3% integrated-framework reduction) are presented without any methodology or results. If the validation were redesigned with a random sample and the missing analyses were supplied, the approach could be of real value; as submitted, the central conclusions rest on a biased benchmark and unverifiable ancillary results.

major comments (4)

[Section 2.5, Tables 5-7] This is the load-bearing issue for the paper's central claim.
[Section 3.6 and Abstract] This is a methodological gap in a key advertised contribution.
[Section 3.2.7] Without this information, the external-validity argument cannot be assessed.
[Sections 2.1, 2.5, and 3.1] This circularity is partly acknowledged in the text but is not carried through to the conclusions.

minor comments (6)

[Section 2.2] The text refers repeatedly to 'tubular data' where 'tabular data' is intended; the typo also appears in the data-description paragraphs and should be corrected throughout.
[Section 3.1 / Equation 2] Equation 2 defines Pe as a sum over raters of p(I_n) * p(NI_n), which is not the standard chance-agreement formula for either pairwise Cohen's kappa or Fleiss' multi-rater kappa. The formula should be corrected or the intended procedure (pairwise vs. multi-rater) should be stated explicitly.
[Section 3.1 / Tables 6-7] The expert survey allowed an 'indeterminate' category, but Tables 6 and 7 report only binary agreement percentages. The paper should state how indeterminate expert responses were treated in the agreement calculations.
[Section 3.3] The sentence 'reducing these errors by 71.5' is missing a percent sign; it should read '71.5%.'
[Table 3] Table 3 reports total numbers and percentages of potentially misclassified crashes but does not state the denominator (presumably the 2019 Iowa crash population or the test subset). The table should be self-contained with the base population stated.
[Section 4] The conclusion calls this 'the first systematic validation of automated narrative classification against expert reviews,' but the validation is based on 100 selected cases. The claim should be qualified to reflect the sample size and selection procedure.

Circularity Check

1 steps flagged · score 6.0 of 10

Expert-validation sample is selected from the model-disagreement cases being ranked (Sec. 2.5), so ALBERT's 73% expert-agreement headline is conditional on a model-built benchmark rather than full-population evidence.

fitted input called prediction [Section 2.5 (Expert Validation and Inter-Rater Agreement); used in Tables 6-8 and the abstract's '73% with Expert 1' claim]
"All model outputs from the test data were analyzed to identify potentially misclassified crashes, defined as instances where intersection crashes were incorrectly labeled as non-intersection or vice versa. Through random stratified sampling, we selected 100 narratives where at least one model's classification differed from the tabular data."

The 100-case validation frame is defined by disagreement between the trained models and the tabular road-type labels, and those tabular labels are the same labels the models were trained to reproduce. The expert benchmark therefore excludes every record where all five models matched the original label and is enriched for cases where model outputs and training labels diverge. ALBERT's 73% agreement with Expert 1 and the model-versus-expert comparisons in Tables 7-8 are conditional on this model-selected disagreement stratum, not an unbiased estimate for the full crash population. The paper's own Tables 5 and 6 expose the selection effect: ALBERT's agreement with tabular labels drops from 88.17% to 58% between the full test set and this subset.

full rationale

The central derivation chain is: train classifiers on the original tabular road-type labels, define 'potentially misclassified crashes' as model disagreement with those labels, select the 100-narrative expert-review sample from that disagreement set, and then rank models by expert agreement on that sample. The load-bearing circular link is the sampling step: the validation set is defined by the model outputs being compared, so the headline ALBERT-versus-expert figures are conditional statistics on a model-selected stratum. This is partial circularity rather than full equivalence, because the expert labels themselves are blinded and independent, so the paper is not identical to its inputs; however, the benchmark's construction from the predicted disagreements prevents the results from supporting the general population claim. The Section 3.6 multi-modal result (92.4% accuracy, 54.2% error reduction) is reported without expert validation and appears to reuse the same original-label agreement definition; I treat that as an evidence gap rather than a separate circular step. Section 4 admits the expert sample 'represents a small portion of the dataset' but does not disclose that it was selected on model disagreement, which is the key unstated limitation. No load-bearing self-citations or imported uniqueness theorems were found, so the circularity concern is confined to the validation-sample construction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper's central claims rely on expert judgment as ground truth, on the integrity of the original DOT labels as training targets, and on an undescribed Minnesota dataset. These are domain assumptions not established by the paper.

free parameters (2)

Model hyperparameters (SVM C=10, gamma=0.01; XGBoost lr=0.05, max_depth=7; BERT/ALBERT learning rates) = tuned via grid search/Bayesian optimization
The reported model rankings could shift under different hyperparameters, but the central ALBERT comparison is probably robust; the more serious issues are the evaluation design and missing analyses.
Domain feature weights in custom lexicon and ambiguity framework = not specified
Section 2.4 and 2.3 describe a 427-term lexicon and ambiguity-related weights applied to terms like 'at' vs 'near' intersection; these weights were evidently tuned on the data but are not reported, and they contribute to the claimed accuracy improvements.

assumptions (4)

domain assumption Expert review of narrative text provides a valid ground truth for intersection-relatedness (Section 2.5).
The paper uses expert classifications as the benchmark for model quality, but experts only reach fair-to-moderate agreement (kappa 0.27-0.50), so the benchmark is noisy.
domain assumption The original tabular roadtype labels are correct enough to serve as training targets and as the 'original label' benchmark (Section 2.2).
Models are trained and evaluated against these labels; if systematic error exists in the labels, both accuracy and expert-agreement comparisons are affected.
ad hoc to paper The Minnesota validation dataset is comparable to the Iowa dataset and was processed with the same pipeline (Section 3.2.7).
No description, source, sample size, or preprocessing details are given for the Minnesota data, yet it is used to claim generalizability.
standard math Standard statistical tests (Cohen's kappa, McNemar, t-tests, chi-square) are applied correctly to the selected samples.
These are standard methods, but the paper does not account for the non-random selection of the 100-narrative sample or multiple testing.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)." pith.science (2026). https://pith.science/paper/OOCUTVSW

@misc{pith2026250703066,
  author       = {Pith},
  title        = {Pith review of: Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OOCUTVSW}},
  note         = {Machine review of arXiv:2507.03066}
}

read the original abstract

This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narratives. Using 2019 crash data from the Iowa Department of Transportation, we implemented and compared a comprehensive set of models, including Support Vector Machine (SVM), XGBoost, BERT Sentence Embeddings, BERT Word Embeddings, and Albert Model. Model performance was systematically validated against expert reviews of potentially misclassified narratives, providing a rigorous assessment of classification accuracy. Results demonstrated that while traditional ML methods exhibited superior overall performance compared to some DL approaches, the Albert Model achieved the highest agreement with expert classifications (73% with Expert 1) and original tabular data (58%). Statistical analysis revealed that the Albert Model maintained performance levels similar to inter-expert consistency rates, significantly outperforming other approaches, particularly on ambiguous narratives. This work addresses a critical gap in transportation safety research through multi-modal integration analysis, which achieved a 54.2% reduction in error rates by combining narrative text with structured crash data. We conclude that hybrid approaches combining automated classification with targeted expert review offer a practical methodology for improving crash data quality, with substantial implications for transportation safety management and policy development.

Figures

Figures reproduced from arXiv: 2507.03066 by the authors.

**Figure 1.** Methodology Flowchart 2.1 Overview of Methodology Before diving into the specific components, we provide a high-level overview of our methodological approach. We obtained crash data and narratives from the Iowa Department of Transportation for 2019, combining and preprocessing the text data to create a unified dataset. The narratives were processed through an NLP pipeline to remove personally identifiable informat… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

14 extracted references · 12 canonical work pages

[1]

Highway Safety Manual

AASHTO (2010). Highway Safety Manual. Technical report, Aashto, Washington ,DC. Abay, K. A. (2015). Investigating the nature and impact of reporting bias in road crash data. Transportation Research Part A: Policy and Practice , 71:31–45. Abdat, F., Leclercq, S., Cuny, X., and Tissot, C. (2014). Extracting recurrent scenarios from narrative texts using a B...

arXiv 2010
[3]

https://highways

Measuring Safety. https://highways. dot.gov/safety/learn-safety/road-safety-fundamentals-html-version/ unit-3-measuring-safety . Accessed: 2025-07-03. Federal Highway Administration (2017b). Unit 3: Measuring Safety – Road Safety Funda- mentals: Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road. Technical report, U.S. Dep...

work page 2017
[13]

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi` ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models. Trueblood, A. B., Pant, A., Kim, J., Kum, H. C., Perez, M., Das, S., and Shipp, E. M. (2019). A semi-automated t...

work page 2023
[14]

C., Ghazzai, H., and Massoud, Y

Wan, X., Lucic, M. C., Ghazzai, H., and Massoud, Y. (2020). Empowering real-time traffic reporting systems with NLP-Processed social media data. IEEE Open Journal of Intelli- gent Transportation Systems, 1:159–175. Wang, Q., Liu, P., Zhu, Z., Yin, H., Zhang, Q., and Zhang, L. (2019a). A text abstraction summary model based on BERT word embedding and reinf...

work page 2020
[119]

Advanced nlp with spacy

DataCamp (2024). Advanced nlp with spacy. https://www.datacamp.com/courses/ advanced-nlp-with-spacy . Accessed: 2024-12-20. Drishti (2022). ALBERT Model for Self-Supervised Learning Beginner. Dube, C. M., Fitzpatrick, C. D., Gazzillo, J. R., and Knodler, M. A. (2016). Improved Identification of Distraction-Related Crashes and the Impact of Distraction-Fre...

work page 2024
[132]

Barnett, M. (2024). Regex. https://github.com/mrabarnett/mrab-regex. GitHub. Re- trieved November 23,

work page 2024
[135]

26 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radfor...

work page 2020
[136]

Pasindu, H. (2019). Importance of road accident data quality. Qiao, J., Wang, C., Guan, S., and Shuran, L. (2022). Construction-accident narrative classi- fication using shallow and deep learning. Journal of Construction Engineering and Man- agement, 148(9):04022088. Rogers, A., Kovaleva, O., and Rumshisky, A. (2020). A Primer in BERTology: What We Know A...

work page 2019

Show all 14 references

[150]

M., Kwigizile, V., and Oh, J.-S

Kwayu, K. M., Kwigizile, V., and Oh, J.-S. (2022). Evaluation of pedestrian crossing- related crashes at undesignated midblock locations using structured crash data and report narratives. Journal of Transportation Safety & Security , 14(1):1–23. Lan, Z., Chen, M., Goodman, S.,...

2022 arXiv
[159]

and Amini, M

Sharifani, K. and Amini, M. (2023). Machine Learning and Deep Learning: A Review of Methods and Applications. Technical report, World Information Technology and Engi- neering Journal. Simha Anirudh (2021). Understanding TF-IDF for Machine Learning — Capital One. Sujon, M. and ...

2023
[164]

R., Lehto, M

Marucci-Wellman, H. R., Lehto, M. R., and Corns, H. L. (2015). A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Na ¨ ıve Bayes algorithms.Accident; analysis and prevention, 84:165–176. M...

2015
[2022]

and Ackaah, W

Salifu, M. and Ackaah, W. (2012). Under-reporting of road traffic crash data in Ghana. International Journal of Injury Control and Safety Promotion , 19(4):331–339. Sarmiento, J. M., Gogineni, A., Bernstein, J. N., Lee, C., Lineen, E. B., Pust, G. D., and Byers, P. M. (2020). ...

2012
[2024]

H., Stones, M

Bedard, M., Guyatt, G. H., Stones, M. J., and Hirdes, J. P. (2002). The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. Accident Analysis & Prevention , 34(6):717–727. Boggs, A. M., Wali, B., and Khattak, A. J. (2020). Exploratory a...

2002
[2025]

and P, P

Khatri, A. and P, P. (2020). Sarcasm Detection in Tweets with BERT and GloVe Embed- dings. In Klebanov, B. B., Shutova, E., Lichtenstein, P., Muresan, S., Wee, C., Feldman, A., and Ghosh, D., editors, Proceedings of the Second Workshop on Figurative Language Processing, pages ...

2020

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Highway Safety Manual

AASHTO (2010). Highway Safety Manual. Technical report, Aashto, Washington ,DC. Abay, K. A. (2015). Investigating the nature and impact of reporting bias in road crash data. Transportation Research Part A: Policy and Practice , 71:31–45. Abdat, F., Leclercq, S., Cuny, X., and Tissot, C. (2014). Extracting recurrent scenarios from narrative texts using a B...

arXiv 2010

[2] [3]

https://highways

Measuring Safety. https://highways. dot.gov/safety/learn-safety/road-safety-fundamentals-html-version/ unit-3-measuring-safety . Accessed: 2025-07-03. Federal Highway Administration (2017b). Unit 3: Measuring Safety – Road Safety Funda- mentals: Concepts, Strategies, and Practices that Reduce Fatalities and Injuries on the Road. Technical report, U.S. Dep...

work page 2017

[3] [13]

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi` ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. (2023). Llama: Open and efficient foundation language models. Trueblood, A. B., Pant, A., Kim, J., Kum, H. C., Perez, M., Das, S., and Shipp, E. M. (2019). A semi-automated t...

work page 2023

[4] [14]

C., Ghazzai, H., and Massoud, Y

Wan, X., Lucic, M. C., Ghazzai, H., and Massoud, Y. (2020). Empowering real-time traffic reporting systems with NLP-Processed social media data. IEEE Open Journal of Intelli- gent Transportation Systems, 1:159–175. Wang, Q., Liu, P., Zhu, Z., Yin, H., Zhang, Q., and Zhang, L. (2019a). A text abstraction summary model based on BERT word embedding and reinf...

work page 2020

[5] [119]

Advanced nlp with spacy

DataCamp (2024). Advanced nlp with spacy. https://www.datacamp.com/courses/ advanced-nlp-with-spacy . Accessed: 2024-12-20. Drishti (2022). ALBERT Model for Self-Supervised Learning Beginner. Dube, C. M., Fitzpatrick, C. D., Gazzillo, J. R., and Knodler, M. A. (2016). Improved Identification of Distraction-Related Crashes and the Impact of Distraction-Fre...

work page 2024

[6] [132]

Barnett, M. (2024). Regex. https://github.com/mrabarnett/mrab-regex. GitHub. Re- trieved November 23,

work page 2024

[7] [135]

26 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radfor...

work page 2020

[8] [136]

Pasindu, H. (2019). Importance of road accident data quality. Qiao, J., Wang, C., Guan, S., and Shuran, L. (2022). Construction-accident narrative classi- fication using shallow and deep learning. Journal of Construction Engineering and Man- agement, 148(9):04022088. Rogers, A., Kovaleva, O., and Rumshisky, A. (2020). A Primer in BERTology: What We Know A...

work page 2019