Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction

Hongjie Ma; Jakub Marek Cebula; Jebraeel Gholinezhad; Mohamed Hassan Abdalla Idris; Shamsul Masum

REVIEW 3 major objections 4 minor 31 references

Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction

T0 review · 3 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read An LSTM fed with productivity-index features and wrapped in inductive conformal prediction posts the lowest out-of-sample error — MAE 29.638 on Volve well PF14 — among four machine-learning forecasters.

desk verdict The abstract promises a forecasting study with ICP and PI features, but the uploaded manuscript is an unrelated spectral clustering paper, so the reported MAE and coverage numbers have zero inspectable support. read the letter →

arxiv 2508.14078 v1 pith:IWRGXK2Z submitted 2025-08-12 cs.LG physics.data-an

Mohamed Hassan Abdalla Idris , Jakub Marek Cebula , Jebraeel Gholinezhad , Shamsul Masum , Hongjie Ma This is my paper · ORCID

classification cs.LGphysics.data-an

keywords oilproductionforecastinglongshort-termmemory(LSTM)productivityindexinductiveconformalpredictionmultivariatetimeseriesuncertaintyquantificationVolvefieldNorne

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 argues that a long short-term memory (LSTM) network fed with productivity-index features from reservoir engineering — the ratio of production rate to pressure drawdown — and paired with inductive conformal prediction is the best of four machine-learning methods for out-of-sample oil production forecasting. On Volve well PF14 the LSTM reaches a mean absolute error of 19.468 on the held-out test and 29.638 on the genuinely future forecast window, beating BiLSTM, GRU, and XGBoost; the same recipe is later validated on Norne well E1H. The conformal wrapper is claimed to turn point forecasts into prediction intervals with valid 95% coverage while making no distributional assumptions, which matters for skewed production data. If the claims hold, operators could get leaner, physics-informed machine-learning pipelines that report uncertainty bounds instead of relying on heavy numerical simulation.

What carries the argument

Productivity Index (PI)-driven features: reservoir-engineering ratios of production rate to pressure drawdown used as model inputs, the component that injects the physics signal into the pipeline and lowers input dimensionality. Long Short-Term Memory (LSTM) network: the recurrent architecture the paper reports as the most accurate forecaster. Inductive Conformal Prediction (ICP): a distribution-free wrapper that measures residuals on a calibration set and converts them into prediction intervals with a stated coverage guarantee (e.g., 95%); it is the mechanism that turns each point forecast into a band with a claimed validity property.

What would settle it

Re-run the PF14 exercise with productivity-index features recomputed using only data up to the forecast start date, keeping the declared train/calibration/test split; if the LSTM's out-of-sample MAE of 29.638 degrades substantially, the published number was aided by look-ahead information. Separately, tabulate the empirical coverage of the claimed 95% intervals on the genuine forecast window: coverage far below 95% would show the conformal guarantee does not carry over to autocorrelated production series.

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Extended reading notes

Core claim

The paper's central claim is that one recipe — productivity-index features as inputs, an LSTM as forecaster, and inductive conformal prediction — beats the alternatives on real oil-field data. The productivity index, the ratio of production rate to pressure drawdown, condenses reservoir behavior into inputs and trims dimensionality compared with conventional numerical simulation workflows. On Volve well PF14 the LSTM posts the lowest MAE of the four models on both the test segment (19.468) and the out-of-sample horizon (29.638), and is then validated on Norne well E1H. Inductive conformal prediction wraps the forecasts in intervals the paper says give 95% coverage without distributional assu

Load-bearing premise

The headline numbers are genuine only if the productivity-index features are built from rate and pressure data strictly before the forecast window, and if the conformal calibration residuals behave exchangeably with the forecast errors — the paper states neither condition explicitly, so look-ahead information cannot yet be ruled out.

Editorial extensions

If this is right

On Volve well PF14, LSTM with PI-driven features beats BiLSTM, GRU, and XGBoost on both the held-out test (MAE 19.468) and the genuine future window (MAE 29.638).
The same recipe is subsequently validated on Norne well E1H, extending the result beyond the well it was tuned on.
Inductive conformal prediction supplies prediction intervals with claimed valid 95% coverage without distributional assumptions, which matters for skewed, non-normal production data.
PI-driven feature selection reduces input dimensionality compared with conventional numerical simulation workflows, so the forecasting pipeline is lighter.
Forecast bias and prediction direction accuracy add two practical lenses — systematic over/under-forecasting and trend-capture ability — beyond raw MAE.

Reading between the lines

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

A decisive external check is to rebuild the PI features with strict timestamps — using only rate and pressure data from before the forecast start — and re-run the PF14 comparison; the paper never states its split boundary, so the 29.638 out-of-sample MAE cannot yet be independently reproduced.
Conformal coverage is guaranteed under exchangeability, which autocorrelated production time series usually violate; a testable extension is recalibrating the conformal scores on rolling or block windows so the 95% band stays valid through production decline.
If the approach holds up, its significance is architectural: physics-derived features let a small ML pipeline substitute for heavy numerical reservoir simulation in short-horizon planning, with the conformal band supplying the risk measure.
The evidence base is three wells (PF14, PF12, E1H), so the natural next experiment is a blind multi-well benchmark with a pre-registered forecast horizon and feature-construction rule.

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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

You should know this before spending any time on it: the abstract describes a hydrocarbon production forecasting study with LSTM, BiLSTM, GRU, XGBoost, productivity-index features, and inductive conformal prediction, with specific numbers (test MAE 19.468, out-of-sample MAE 29.638). The attached full text is a completely different paper, 'Explainable Graph Spectral Clustering for GloVe-like Text Embeddings,' by different authors, with its own arXiv identifier. So the abstract is the only evidence for every claim about forecasting.

What is actually new or good? The abstract's idea—using PI-derived features plus ICP for calibrated intervals on production forecasts—is a plausible engineering combination, but it is not novel in any method sense. ICP is well established, and the four models are standard. The abstract provides no citations to prior work, so even the intended novelty cannot be located. The body of the manuscript appears to be a coherent paper about spectral clustering and GloVe embeddings, but that is irrelevant to the claimed subject.

The soft spots are not subtle. This is a complete evidentiary gap for the central claim. There is no method section, no data split description, no ICP calibration window, no hyperparameters, no code, no additional results. The reader's concern about possible look-ahead leakage in the PI features is real, but it cannot even be checked because the feature construction is absent. Likewise, the ICP exchangeability assumption for time series is a known caveat, but again there is nothing to evaluate. This is not a paper with a weak section; it is a submission where the text does not match the abstract.

The bottom line: as submitted, this is not a usable paper for the forecasting community. The authors may have uploaded the wrong file or accidentally cross-linked a different arXiv ID, but we can only review what is in front of us. A serious editor would not send this to peer review in its current form; the correct action is to return it to the authors for a corrected submission, and then evaluate the actual forecasting manuscript.

For your own work: do not cite this, and do not bring it to reading group. There is nothing here to engage with on the forecasting side.

Referee Report

3 major / 4 minor

Summary. The abstract of arXiv:2508.14078 describes an out-of-sample hydrocarbon production forecasting framework combining Productivity Index (PI)-driven feature engineering, several machine-learning models (LSTM, BiLSTM, GRU, XGBoost), and Inductive Conformal Prediction (ICP) for uncertainty quantification on Volve and Norne field data. It reports an LSTM test MAE of 19.468 and an out-of-sample MAE of 29.638 for well PF14, together with 95% prediction intervals claimed to be guaranteed by ICP. However, the full text attached to the manuscript is a completely different paper, 'Explainable Graph Spectral Clustering For GloVe-like Text Embeddings' by Kłopotek et al., with a different title, author list, abstract, and technical content. None of the forecasting methodology, data splits, PI feature definitions, ICP calibration procedure, experimental setup, or numerical results described in the abstract appear anywhere in the body.

Significance. If the abstract's claims were supported, the paper would offer a useful combination of domain-specific feature construction with distribution-free conformal prediction for production forecasting, and the reported numerical comparisons would be of empirical interest. I can identify no such deliverable in the submitted manuscript, however. There is no inspectable derivation, no reproducible code, no dataset-preprocessing description, no hyperparameter or split specification, and no validation of the conformal coverage claim. The body's spectral-clustering derivations are unrelated to the abstract's subject. Thus the significance of the claimed result cannot be evaluated; at present the manuscript does not supply a forecast study at all.

major comments (3)

[Full text (title page and Sections 1–14)] The manuscript body does not correspond to the abstract. The full text is 'Explainable Graph Spectral Clustering For GloVe-like Text Embeddings' by M. A. Kłopotek et al., with no mention of hydrocarbon production, PI features, LSTM/BiLSTM/GRU/XGBoost, ICP, Volve, Norne, or the reported MAE values (19.468, 29.638). Consequently none of the central claims can be inspected: feature temporal construction, data splits, model training and hyperparameters, calibration, and coverage are all absent. This is a complete evidentiary gap for the paper's central result, not a local presentation issue.
[Abstract, ICP sentence] The statement that ICP 'guarantees valid prediction intervals (e.g., 95% coverage) without reliance on distributional assumptions' is not valid as stated for out-of-sample time-series forecasting. Split-conformal inference requires exchangeability between calibration scores and the test score; with temporally ordered production data this assumption generally fails unless the series is treated as stationary or a specialized scheme is used. The abstract gives no calibration split, significance level, or condition under which the guarantee holds. The 95% coverage claim is therefore at best an unverifiable empirical assertion.
[Abstract, 'genuine out-of-sample' forecast] PI-driven features are defined in reservoir engineering from production rate and pressure drawdown, but the abstract never states that the pressure/rate inputs used to construct features at forecast time are restricted to information available before the forecast horizon. If data from the forecast period enter the feature construction, the LSTM out-of-sample MAE of 29.638 is not a genuine forecast. Because the manuscript body contains no feature-construction or temporal-split description, this look-ahead leakage risk cannot be ruled out. The model comparison also provides no variance estimates or repeated-seed results.

minor comments (4)

[Abstract] The phrase 'out-of-sample' is inconsistently quoted and used. The abstract should define the exact temporal split, the gap between training/calibration and test/forecast periods, and what distinguishes 'test' from 'genuine out-of-sample forecast'.
[General] The title, author list, and abstract do not match the body. This needs administrative correction; as submitted, the reader cannot tell which document is intended.
[General] No code, data, or reproducibility statement is provided, and no versions or licenses are cited for the Volve and Norne datasets. Such identifiers are essential for any forecasting comparison.
[Abstract] The conformal prediction claim lacks references to the standard literature (Vovk et al.; split conformal; weighted exchangeability for time series). The term 'guarantees' should be qualified to state the assumptions under which coverage holds.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation identified; the abstract's forecasting claims are unsupported by the supplied full text, which is an unrelated spectral-clustering paper.

full rationale

The abstract (arXiv:2508.14078) claims an LSTM/PI/ICP hydrocarbon forecasting study with specific MAEs ('lowest MAE on test (19.468) and genuine out-of-sample forecast data (29.638) for well PF14') and ICP coverage guarantees. The full text provided, however, is 'Explainable Graph Spectral Clustering For GloVe-like Text Embeddings' by Kłopotek et al. (arXiv:2508.14075v2). It contains no PI features, no LSTM/BiLSTM/GRU/XGBoost experiments, no Volve/Norne wells, and no ICP calibration or split description. There is therefore no derivation chain from inputs to the abstract's forecast numbers; the claimed results are asserted but not derived. Under the reviewing rule, this is explicitly flagged as a missing-support defect, located in the abstract vs. title/full-text mismatch. Missing support is distinct from circularity: hard rule 1 requires quoting a specific reduction (Eq. X = Eq. Y by construction, or fitted parameter renamed as prediction), and no such reduction can be exhibited because the forecasting methodology is absent. The ICP 'guarantee' is a standard distribution-free theorem rather than a circular redefinition. No self-citation is load-bearing for the abstract's claims. Accordingly the circularity score is 0, with the caveat that the paper as supplied fails a basic evidentiary check independent of circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The ledger is small because the abstract is short: three classes of tuned or underspecified numbers (model hyperparameters, ICP level, PI feature settings) and three domain assumptions (exchangeability, no look-ahead in PI features, data representativeness). No new entities are postulated; PI is standard reservoir engineering. The numbers the paper reports (MAE 19.468, 29.638) are outputs, not fitted parameters, but the process that produced them is undescribed, so the parameter audit cannot be completed.

free parameters (3)

LSTM/BiLSTM/GRU/XGBoost hyperparameters
Tuned to produce the reported best MAE values; the tuning procedure and values are absent from the provided text.
ICP significance level (alpha for 95% intervals)
The abstract claims 95% coverage; the chosen alpha and the calibration set size that deliver this coverage are not reported.
PI feature construction parameters (time windows, pressure and rate inputs)
The abstract says PI-driven features were selected but does not define the time windows or inputs; the temporal alignment of these features is the weakest assumption.

assumptions (3)

domain assumption Exchangeability of the training, calibration, and forecast data (the ICP validity condition)
The abstract states ICP 'guarantees valid prediction intervals (e.g., 95% coverage)'; that guarantee requires exchangeability, which production time series rarely satisfy. The abstract does not discuss this limitation.
domain assumption PI features are computable without look-ahead information at forecast time
Productivity index is defined from rate and pressure drawdown; if pressure observations inside the forecast horizon feed the features, the out-of-sample claim breaks. The abstract never states the feature time alignment.
domain assumption The historical production records of Volve and Norne are reliable and representative of future behavior
Forecast validity is only as good as the public well data and the assumption that their statistical behavior continues into the forecast window.

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Cite this review

Pith. "Pith review of Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction." pith.science (2026). https://pith.science/paper/IWRGXK2Z

@misc{pith2026250814078,
  author       = {Pith},
  title        = {Pith review of: Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IWRGXK2Z}},
  note         = {Machine review of arXiv:2508.14078}
}

read the original abstract

This research introduces a new ML framework designed to enhance the robustness of out-of-sample hydrocarbon production forecasting, specifically addressing multivariate time series analysis. The proposed methodology integrates Productivity Index (PI)-driven feature selection, a concept derived from reservoir engineering, with Inductive Conformal Prediction (ICP) for rigorous uncertainty quantification. Utilizing historical data from the Volve (wells PF14, PF12) and Norne (well E1H) oil fields, this study investigates the efficacy of various predictive algorithms-namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and eXtreme Gradient Boosting (XGBoost) - in forecasting historical oil production rates (OPR_H). All the models achieved "out-of-sample" production forecasts for an upcoming future timeframe. Model performance was comprehensively evaluated using traditional error metrics (e.g., MAE) supplemented by Forecast Bias and Prediction Direction Accuracy (PDA) to assess bias and trend-capturing capabilities. The PI-based feature selection effectively reduced input dimensionality compared to conventional numerical simulation workflows. The uncertainty quantification was addressed using the ICP framework, a distribution-free approach that guarantees valid prediction intervals (e.g., 95% coverage) without reliance on distributional assumptions, offering a distinct advantage over traditional confidence intervals, particularly for complex, non-normal data. Results demonstrated the superior performance of the LSTM model, achieving the lowest MAE on test (19.468) and genuine out-of-sample forecast data (29.638) for well PF14, with subsequent validation on Norne well E1H. These findings highlight the significant potential of combining domain-specific knowledge with advanced ML techniques to improve the reliability of hydrocarbon production forecasts.

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Reference graph

Works this paper leans on

31 extracted references · 29 canonical work pages

[1]

SimCompass: Using deep learning word embeddings to assess cross-level similarity

Carmen Banea, Di Chen, Rada Mihalcea, Claire Cardie, and Janyce Wiebe. SimCompass: Using deep learning word embeddings to assess cross-level similarity. In Preslav Nakov and Torsten Zesch, editors, �� , pages 560–565, Dublin, Ireland, August 2014. Association for Computa- tion...

work page 2014
[2]

Document clustering: A review.�� , 73(11):26–33, July 2013

Sunita Bisht and Amit Paul. Document clustering: A review.�� , 73(11):26–33, July 2013

work page 2013
[3]

Representation learning for very short texts using weighted word embedding aggregation

Cedric De Boom, Steven Van Canneyt, Thomas Demeester, and Bart Dhoedt. Representation learning for very short texts using weighted word embedding aggregation. �� , 80, 2016

work page 2016
[4]

Text similarity estimation based on word embeddings and matrix norms for targeted marketing

Tim vor der Brück and Marc Pouly. Text similarity estimation based on word embeddings and matrix norms for targeted marketing. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, �� ...

work page 2019
[5]

API design for machine learning software: experiences from the scikit-learn project

Lars Buitinck, Gilles Louppe, Mathieu Blondel, et al. API design for machine learning software: experiences from the scikit-learn project. In �� , pages 108–122, 2013.��

work page 2013
[6]

BERT: Pre-training of deep bidirectional transformers for language un- derstanding, 2019

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language un- derstanding, 2019. ��

work page 2019
[7]

Dhillon, Yuqiang Guan, and Brian Kulis

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. Kernel k-means: Spec- tral clustering and normalized cuts. In �� , KDD ’04, pages 551–556, New York, NY, USA, 2004. ACM

work page 2004
[8]

Measuring text similarity based on structure and word embedding

Mamdouh Farouk. Measuring text similarity based on structure and word embedding. �� , 63:1–10, 2020. 45

work page 2020

Show all 31 references

[9]

John C. Gower. Some distance properties of latent root and vector methods used in multivariate analysis.��, 53(3-4):325–338, 1966

1966
[10]

Harris, K

Charles R. Harris, K. Jarrod Millman, Stefan van der Walt, et al. Array programming with NumPy. ��, 585:357–362, 2020. ��

2020
[11]

Convolutional neural network architectures for matching natural language sentences

Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. Convolutional neural network architectures for matching natural language sentences. In �� , NIPS’14, page 2042–2050, Cambridge,...
[12]

Short text similarity with word em- beddings

Tom Kenter and Maarten de Rijke. Short text similarity with word em- beddings. In �� , CIKM ’15, pages 1411–1420, New York, NY, USA, 2015. ACM

2015
[13]

clustering4docs github repository,

Hyunjoong Kim and Han Kyul Kim. clustering4docs github repository,
[14]

Improving spherical k-means for document clustering: Fast initialization, sparse centroid pro- jection, and efficient cluster labeling

Hyunjoong Kim, Han Kyul Kim, and Sungzoon Cho. Improving spherical k-means for document clustering: Fast initialization, sparse centroid pro- jection, and efficient cluster labeling. �� , 150:113288, 2020

2020
[15]

Kłopotek, and Sławomir T

Robert Kłopotek, Mieczysław A. Kłopotek, and Sławomir T. Wierzchoń. A feasible k-means kernel trick under non-euclidean feature space.�� , 30(4):703– 715, 2020. Online publication date: 1-Dec-2020

2020
[16]

Kłopotek, Sławomir T

Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Bartłomiej Starosta, Dariusz Czerski, and Piotr Borkowski. A method for handling negative similarities in explainable graph spectral clustering of text documents – extended version, 2025.��

2025
[17]

An empirical evaluation of doc2vec with practical insights into document embedding generation

Jey Han Lau and Timothy Baldwin. An empirical evaluation of doc2vec with practical insights into document embedding generation. arXiv:1607.05368v1 [cs.CL], 2016

2016 arXiv
[18]

S. Lloyd. Least squares quantization in PCM. �� , 28(2):129–137, March 1982

1982
[19]

A tutorial on spectral clustering.�� , 17(4):395–416, 2007

Ulrike von Luxburg. A tutorial on spectral clustering.�� , 17(4):395–416, 2007

2007
[20]

LIM-LIG at SemEval-2017 task1: Enhancing the semantic similarity for Arabic sen- tences with vectors weighting

El Moatez Billah Nagoudi, Jérémy Ferrero, and Didier Schwab. LIM-LIG at SemEval-2017 task1: Enhancing the semantic similarity for Arabic sen- tences with vectors weighting. In Steven Bethard, Marine Carpuat, Mar- ianna Apidianaki, Saif M. Mohammad, Daniel Cer, and David Jurgen...

2017
[21]

GloVe: Global vectors for word representation

Jeffrey Pennington, Richard Socher, and Christopher Manning. GloVe: Global vectors for word representation. In Alessandro Moschitti, Bo Pang, and Walter Daelemans, editors, �� , pages 15...

2014
[22]

word2vec parameter learning explained, 2014

Xin Rong. word2vec parameter learning explained, 2014. arXiv:1411.2738 [cs.CL]

2014 arXiv
[23]

Salton, A

G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. �� , 18(11):613–620, 1975

1975
[24]

Manning, and Andrew Y

Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng. Reasoning with neural tensor networks for knowledge base completion. In �� , NIPS’13, page 926–934, Red Hook,...

2013
[25]

Kłopotek, and Sławomir T

Bartłomiej Starosta, Mieczysław A. Kłopotek, and Sławomir T. Wierz- choń. Approaches to Explainability of output of graph spectral clustering methods, 2024. submitted

2024
[26]

Kłopotek, Sławomir T

Bartłomiej Starosta, Mieczysław A. Kłopotek, Sławomir T. Wierzchoń, Dariusz Czerski, Marcin Sydow, and Piotr Borkowski. Explainable graph spectral clustering of text documents. �� , 20(2):e0313238, February 2025. https://journals.plos.org/plosone/article?id=10.1371/journ...

2025 doi
[27]

Oliphant, et al

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al. SciPy 1.0: Fun- damental Algorithms for Scientific Computing in Python.�� , 17:261–272, 2020. ��

2020
[28]

Wierzchoń and M.A

S.T. Wierzchoń and M.A. Kłopotek.�� , volume 34 of�� . Springer Verlag, 2018

2018
[29]

Effective and efficient spectral clustering on text and link data

Zhiqiang Xu and Yiping Ke. Effective and efficient spectral clustering on text and link data. In�� , pages 357—-366, 2016

2016
[30]

From word embeddings to document similarities for improved information retrieval in software engineering

Xin Ye, Hui Shen, Xiao Ma, Razvan Bunescu, and Chang Liu. From word embeddings to document similarities for improved information retrieval in software engineering. In �� , ICSE ’16, page 404–415, Ne...

2016
[2020]

��

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

SimCompass: Using deep learning word embeddings to assess cross-level similarity

Carmen Banea, Di Chen, Rada Mihalcea, Claire Cardie, and Janyce Wiebe. SimCompass: Using deep learning word embeddings to assess cross-level similarity. In Preslav Nakov and Torsten Zesch, editors, �� , pages 560–565, Dublin, Ireland, August 2014. Association for Computa- tion...

work page 2014

[2] [2]

Document clustering: A review.�� , 73(11):26–33, July 2013

Sunita Bisht and Amit Paul. Document clustering: A review.�� , 73(11):26–33, July 2013

work page 2013

[3] [3]

Representation learning for very short texts using weighted word embedding aggregation

Cedric De Boom, Steven Van Canneyt, Thomas Demeester, and Bart Dhoedt. Representation learning for very short texts using weighted word embedding aggregation. �� , 80, 2016

work page 2016

[4] [4]

Text similarity estimation based on word embeddings and matrix norms for targeted marketing

Tim vor der Brück and Marc Pouly. Text similarity estimation based on word embeddings and matrix norms for targeted marketing. In Jill Burstein, Christy Doran, and Thamar Solorio, editors, �� ...

work page 2019

[5] [5]

API design for machine learning software: experiences from the scikit-learn project

Lars Buitinck, Gilles Louppe, Mathieu Blondel, et al. API design for machine learning software: experiences from the scikit-learn project. In �� , pages 108–122, 2013.��

work page 2013

[6] [6]

BERT: Pre-training of deep bidirectional transformers for language un- derstanding, 2019

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language un- derstanding, 2019. ��

work page 2019

[7] [7]

Dhillon, Yuqiang Guan, and Brian Kulis

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. Kernel k-means: Spec- tral clustering and normalized cuts. In �� , KDD ’04, pages 551–556, New York, NY, USA, 2004. ACM

work page 2004

[8] [8]

Measuring text similarity based on structure and word embedding

Mamdouh Farouk. Measuring text similarity based on structure and word embedding. �� , 63:1–10, 2020. 45

work page 2020