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REVIEW 5 major objections 6 minor 52 references

UncertaintyVis: Preserving Linguistic Uncertainty in Automated Text-to-Chart Generation

T0 review · 5 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Hedged numbers like 'nearly' and 'at least' can survive automated chart generation: readers decode them back to the source text with 85% accuracy, and the paper reports that these uncertainty-aware charts trend toward lower mental demand…

desk verdict Real gap and a plausible taxonomy, but the matching study is too confounded to support the central claim. read the letter →

arxiv 2608.07093 v1 pith:UR4KX36K submitted 2026-08-07 cs.HC

Songheng Zhang , Emily Aurelia , Anthony Tang This is my paper · ORCID
classification cs.HC
keywords linguisticuncertaintyvisualizationtext-to-chartgenerationdata-richdocumentssemanticpreservationvisualencodinglargelanguagemodels
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 automated text-to-chart systems should preserve, not discard, the linguistic hedges authors attach to numbers such as 'nearly', 'approximately', and 'between'. It builds UncertaintyVis, a pipeline that classifies hedged statements into a four-part taxonomy and renders each type with a distinct visual cue, while keeping the underlying numbers spatially accurate. In a 12-participant study, readers matched charts back to source text 85% of the time and text to charts 76% of the time, with a non-significant trend toward lower mental demand and a 75% stated preference for the uncertainty-enhanced charts. If the claim holds, automated tools would stop turning deliberately imprecise prose into false-precision graphics.

What carries the argument

The load-bearing mechanism is the mapping from a four-category taxonomy of linguistic uncertainty to deterministic visual encodings: squiggly borders for approximation, diagonal hatching plus interactive question-mark badges for inferred values, darker thicker borders with hover-revealed originals for normalized formats, and N/A labels for missing data. A large language model classifies extracted text into these categories and outputs structured JSON metadata; a renderer applies the encodings on top of standard charts. The taxonomy itself comes from a corpus analysis of 211 uncertainty expressions across 12 documents and 8 domains, and the encoding choices draw on sketchiness and transparency conventions from uncertainty visualization.

What would settle it

A controlled study that strips the numeric anchors from the uncertainty encodings, such as showing a squiggly bar with no axis labels and asking participants to pick the matching verbal hedge, would show whether the visual cues alone communicate uncertainty; if accuracy collapses without the numbers, the 85% and 76% figures reflect number-matching plus taught mappings rather than the encodings themselves. A second decisive check would remove the orientation step or test with charts drawn from a held-out corpus.

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Extended reading notes

Core claim

The paper's central claim is that linguistic uncertainty can be preserved in automated chart generation without sacrificing the spatial accuracy readers rely on. The authors derive a four-category taxonomy from a 12-document corpus (Inferential Derivation, Precision Boundaries, Surface Form Normalization, Non-Inferable Gaps), map each category to chart-specific visual encodings, and implement an end-to-end system that uses a large language model to extract data, classify uncertainty, and render encoded charts. In their evaluation, readers matched charts to source text at 85% accuracy and text to charts at 76%, and uncertainty-aware visualizations trended toward lower cognitive demand; 75% of participants preferred them to plain text. The paper also finds that bar and pie encodings work consistently while line chart encodings need redesign.

Load-bearing premise

The evaluation assumes that the measured matching accuracy shows the visual encodings themselves carry the uncertainty semantics, but participants may have relied on simpler cues: they received an orientation on the taxonomy before Part 1, the Part 2 documents come from the same 12-document corpus used to build the taxonomy, and participants described a Match-Verify strategy that checks exact numeric alignment before evaluating uncertainty markers.

Editorial extensions

If this is right

  • Automated text-to-chart systems that adopt this approach will stop rendering 'nearly 70,000 m²' as an exact 70,000 m², reducing the false-precision effect in data journalism, policy briefs, and medical reports.
  • Readers of uncertainty-encoded charts can verify claims against source text, effectively turning the visualization into checkable evidence rather than an authoritative-looking graphic.
  • Bar and pie chart encodings are likely to work in deployed systems; line charts need new encodings such as confidence ribbons or interval markers before coverage is complete.
  • The trend toward lower mental demand at equal reading time suggests uncertainty annotations can be added without a speed or comprehension penalty, warranting a larger replication study.
  • The taxonomy gives NLP-based chart generation a target structure: instead of erasing hedges during extraction, the pipeline outputs structured uncertainty metadata that downstream renderers consume.

Reading between the lines

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

  • The same taxonomy could extend beyond numerical hedges to epistemic modal language such as 'may', 'likely', and 'suggests', which the paper names as future work but does not implement; visualizing those would cover a much larger share of real-world hedging.
  • If uncertainty-preserving charts become a convention, they could function as a lightweight verification standard for data journalism, analogous to source citations, a consequence the paper's 'trust infrastructure' framing gestures toward but leaves undeveloped.
  • A natural testable extension is automatic detection of uncertainty expressions, since the current system requires manual selection of excerpts; integrating detection would let the approach scale to full documents and would test whether the taxonomy is stable outside the corpus used to derive it.
  • The line-chart failure suggests a broader design principle: uncertainty encodings built for discrete marks such as bars and slices may not transfer to continuous marks such as lines and areas, so each mark type needs its own encoding palette worth testing across area charts and scatterplots.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper introduces UncertaintyVis, an end-to-end pipeline that preserves linguistic uncertainty in automated text-to-chart generation. A formative corpus analysis of 211 uncertainty expressions across 12 documents and 8 domains yields a four-category taxonomy (Inferential Derivation, Precision Boundaries, Surface Form Normalization, Non-Inferable Gaps), which is mapped to chart-specific visual encodings and implemented using an LLM-based extraction pipeline. A two-part user study with 12 participants reports 85% chart-to-text and 76% text-to-chart matching accuracy, non-significant trends toward reduced cognitive workload, and 75% preference for uncertainty-aware visualizations. The paper's central claim is that these results demonstrate that visual encodings successfully communicate uncertainty semantics and that preserving linguistic uncertainty is practically beneficial.

Significance. If the central claim were established, the paper would address a real gap in text-to-chart systems: current tools flatten hedged expressions such as 'nearly 70,000 m²' into false precision. The strengths of the work are concrete: the corpus analysis is described in detail, the taxonomy is defined with examples, the system is implemented, and the study materials are reproduced in the appendices, which supports replication. The authors also candidly report the failures of line-chart encodings, gradient range markers, and N/A labels, and they do not hide non-significant results. However, I agree with the stress-test concern: the primary evidence for encoding effectiveness, the Part 1 matching accuracies, is confounded by the orientation procedure, the use of the same corpus for taxonomy development and evaluation, and the availability of number-based filtering in the matching tasks. As a result, the headline accuracy numbers do not yet establish that the visual encodings themselves carry the uncertainty semantics.

major comments (5)
  1. [Section 6.5] Section 6.5 states that Part 1 began with an orientation explaining the taxonomy categories and their visual encodings. Consequently, the reported 76% text-to-chart and 85% chart-to-text accuracies in Section 7.1.1 may reflect participants' ability to reproduce mappings they were just taught, rather than demonstrating that the visual encodings independently communicate uncertainty semantics. The central claim in Section 7.1.1 that 'our visual encoding strategies successfully communicate uncertainty semantics' therefore needs an evaluation condition without such orientation, or a transfer test to unseen expressions and encodings, to separate learning from decoding.
  2. [Section 6.3] Section 6.3 states that Part 2 documents were drawn directly from the 12-document corpus analyzed in the formative study, and Section 3.3 describes how the taxonomy was developed and refined on that same corpus. Evaluating the taxonomy on the same documents from which it was derived overestimates its generalizability. The authors should validate the taxonomy on held-out documents from different sources, or at minimum report which documents were used for taxonomy development versus evaluation and provide a per-document breakdown of the matching results.
  3. [Section 7.3.1 and Appendix B] Section 7.3.1 reports that participants used a Match-Verify strategy, first checking exact numeric alignment before evaluating uncertainty markers. Inspection of Appendix B shows that the multiple-choice options frequently differ in numerical values (e.g., chart-to-text Q1, Q2, Q3, Q5, Q8, and many text-to-chart items), so a participant can select the correct option by number matching alone without decoding the uncertainty encoding. The reported 85% and 76% accuracies therefore do not establish that the visual encodings themselves carried the uncertainty semantics. The authors should either redesign distractors to hold numerical values constant or report accuracy separately for trials in which numeric filtering is insufficient to identify the correct answer.
  4. [Section 4.1 and Appendix A.1] Section 4.1 defines the proximity levels for directional comparisons as 3%, 5%, and 8% spatial differences, and Figure 5 uses the same values, but Appendix A.1 Prompt A instructs the LLM to use Close (3% offset), Moderate (5% offset), and Large (10% offset). This inconsistency means the implemented system may render directional comparisons with a 10% rather than 8% offset, contradicting the stated semantic-to-spatial mapping. The encoding specification and the LLM prompt must be reconciled, and any generated stimuli in the user study should be checked against the intended offsets.
  5. [Section 7.2.2] The two workload results that most directly support the practical-benefit claim are not statistically significant (mental demand p=0.054, effort p=0.073). The sentence in Section 7.2.2 stating that 'when fully powered studies replicate this work, these trends will likely achieve statistical significance' is not an empirical finding but a speculative extrapolation. The authors should present these results as suggestive trends that require replication, and should temper the corresponding claims in the abstract and Section 9.
minor comments (6)
  1. [Section 5.1] Section 5.1 contains an unresolved cross-reference: 'the large language model based uncertainty-aware pipeline (Section ??).' The section number should be inserted.
  2. [Section 7.1.3] In Section 7.1.3, 'diagonal hathcing ppatern or shaded bars' contains a typo; it should read 'diagonal hatching pattern'.
  3. [Throughout] The category name is spelled inconsistently: 'Non-Inferable Gaps' appears in the main text and figures, while Appendix A and the JSON schema use 'non_inferrable_gaps' and 'Non-Inferrable Gaps'. The spelling should be standardized.
  4. [Section 4.2] Section 4.2 refers to 'Figure 5i' when describing line chart adaptations, but the caption of Figure 5 labels the line chart adaptations as 'j-l'. The cross-reference should be corrected.
  5. [Table 1] In Table 1, question Q9 is a vertical bar chart with 67% accuracy, which is below the 60% threshold used to gray out rows in the table; please verify that the row-shading rule is applied consistently.
  6. [Section 7.1.2] The semantic suitability ratings are reported as averages such as '6 out of 7'; consider reporting standard deviations or full distributions so that the central tendency can be interpreted alongside the matching accuracy results.

Circularity Check

3 steps flagged · score 5.0 of 10

Part 1 matching accuracy is confounded by orientation teaching and numeric filtering, so the claim that the encodings communicate uncertainty semantics reduces partly to taught mappings and number matching.

  1. fitted input called prediction [Section 6.5 Procedure; Section 7.1.1 Overall Matching Accuracy]
    "Part 1 began with orientation explaining the taxonomy categories and their visual encodings to establish shared understanding. ... The higher chart-to-text accuracy therefore indicates that our visual encoding strategies successfully communicate uncertainty semantics once presented to readers."

    The central evidence that the encodings 'successfully communicate uncertainty semantics' is collected after the experimenter explicitly taught participants the taxonomy-to-encoding mapping. High accuracy then reflects reproduction of just-taught conventions rather than independent decoding of the visual variables. The paper itself concedes the result holds 'once presented to readers' and even suggests 'explicit onboarding or guided familiarization,' which confirms that the measured matching performance is partly a test of taught associations, not of the encodings carrying the semantics on their own.

  2. other [Section 7.1.3 Chart-to-Text Performance Analysis; Section 7.3.1 The Match-Verify Cognitive Strategy]
    "Participants employed an 'elimination strategy' where they quickly identified obvious numerical mismatches before evaluating uncertainty semantics. P8 explained: "Usually I'll take a look at the bullet text first, then see if the number matches, and then try to infer the words around it.""

    The paper's own participant strategy description shows that chart-to-text accuracy can be achieved by numeric alignment alone. Several Appendix B distractors differ in exact numbers (e.g., 700,100 vs. 'a little more than 700,000'; 105 vs. 104; 534,000 vs. 'less than 534,000'), so participants can eliminate options before interpreting any uncertainty encoding. The claimed 85% chart-to-text accuracy therefore does not establish that the visual encodings communicate uncertainty semantics; it is partly reducible to number matching, which the paper itself identifies as the first filtering stage.

1 more flagged steps
  1. other [Section 6.3 Materials and Apparatus]
    "These documents were drawn directly from the 12-document corpus analyzed in the formative study."

    The four-category taxonomy was derived from this same 12-document corpus in the formative study. Using documents from that same corpus to evaluate comprehension, preference, and workload means the Part 2 utility evidence is in-sample: it demonstrates effects on the very texts that generated the taxonomy, not on held-out documents. This weakens the generalizable claim that uncertainty-aware visualizations produce practical comprehension benefits beyond the corpus that shaped the design.

full rationale

There is no formal deductive circularity in the pipeline: the taxonomy is adapted from QIE categories and corpus analysis, the encodings are grounded in external uncertainty-visualization literature, and the system implementation does not derive its conclusions from its own definitions. However, the paper's headline empirical claims rest on a circular evaluation structure. First, Part 1 matching accuracy (85% and 76%) is treated as evidence that the visual encodings 'successfully communicate uncertainty semantics,' but participants were explicitly taught the taxonomy categories and their visual encodings during orientation before the task, so the task partly measures reproduction of taught mappings. Second, the paper's own Match-Verify qualitative finding shows participants first check numeric alignment and only then evaluate uncertainty markers; many chart-to-text distractors differ numerically, so 85% accuracy can be achieved without decoding the uncertainty encoding at all. Third, Part 2 documents are drawn from the same 12-document corpus used to derive the taxonomy, making the utility evaluation in-sample. These issues undermine the central claim that the encodings themselves carry the uncertainty semantics. The paper still has independent content: the system is implemented, several encodings are grounded in prior visualization work, and the qualitative design-failure results (gradients, NA labels, line charts) provide non-circular evidence for iteration. Hence the score is moderate, reflecting partial circularity in the evaluation rather than a fully self-referential derivation.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central contribution is a design framework and an empirical evaluation, not a mathematical derivation. The main hand-chosen quantities are the proximity offsets for directional hedges, plus the analytical choice of a four-category taxonomy from a small corpus. No new physical entities are introduced.

free parameters (1)
  • proximity_offsets_close_moderate_large = 3%, 5%, 8% per Section 4.1; Appendix A.1 lists large as 10%
    Hand-chosen spatial offsets mapping 'slightly greater than', 'greater than', and 'substantially greater' to visual extension heights. The values are not empirically fitted, and the paper contains an internal inconsistency.
assumptions (4)
  • domain assumption The 12-document, 8-domain corpus adequately spans the space of linguistic uncertainty in data-rich documents.
    The four-category taxonomy is derived from this corpus without independent corpus validation (Section 3.1).
  • ad hoc to paper Linguistic intensity of directional hedges can be discretized into three proximity levels with fixed percentage offsets.
    Section 4.1 assigns 3%, 5%, and 8% offsets; the appendix prompt uses 10% for the large level. No empirical basis is given for these constants.
  • domain assumption Visual conventions such as squiggly borders, hatching, and question mark badges are intuitive and culturally available to readers.
    The encoding design in Section 4 assumes these conventions carry meaning; the user study partially confirms squiggles and hatching but strongly rejects gradients and N/A labels.
  • domain assumption GPT-4 extraction and uncertainty classification are sufficiently accurate to support the pipeline.
    The LLM output quality is not evaluated separately from the visualization study (Section 5.2), and manual pre-selection of expressions sidesteps detection failures.

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Cite this review

Pith. "Pith review of UncertaintyVis: Preserving Linguistic Uncertainty in Automated Text-to-Chart Generation." pith.science (2026). https://pith.science/paper/UR4KX36K

@misc{pith2026260807093,
  author       = {Pith},
  title        = {Pith review of: UncertaintyVis: Preserving Linguistic Uncertainty in Automated Text-to-Chart Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UR4KX36K}},
  note         = {Machine review of arXiv:2608.07093}
}
read the original abstract

Data-rich documents pair narrative text with quantitative claims, and authors routinely qualify those claims with linguistic uncertainty markers such as "nearly," "approximately," or "at least." Automated text-to-chart systems discard these markers, producing visualizations that appear definitive even when the source text expresses hedged or incomplete knowledge. Readers may then over-interpret precision and misjudge author intent. We present UncertaintyVis, a system that preserves linguistic uncertainty during automated chart generation. A formative corpus analysis of 211 uncertainty expressions across 12 documents and 8 domains yielded a four-category taxonomy: Surface Form Normalization, Precision Boundaries, Inferential Derivation, and Non-Inferable Gaps. We mapped each category to chart-specific visual encodings that signal uncertainty without disturbing the spatial integrity readers rely on, and implemented an end-to-end pipeline pairing large language model text analysis with uncertainty-aware rendering. In a two-part study with 12 participants, readers matched charts to source text with 85% accuracy and text to charts with 76%. Uncertainty-aware visualizations trended toward lower cognitive demand (effect sizes 0.460 and 0.769 for mental demand and effort), and 75% of participants preferred them to plain text, describing explicit uncertainty encodings as a basis for verifying data claims. Encoding effectiveness varied by chart type: bar and pie encodings performed consistently, while line chart encodings require redesign.

Figures

Figures reproduced from arXiv: 2608.07093 by the authors.

Figure 1
Figure 1. Current text-to-chart systems discard textual uncertainty from source text. a, Source text describing Hesai’s R&D [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. We analyzed 211 uncertainty expressions across 12 documents spanning 8 domains and 3 genres to establish empirical [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Interactive explainability for inferentially derived [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figures from the paper (6 more)
Figure 6
Figure 6. Figure 6: Interactive transparency for surface form nor [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 8
Figure 8. Figure 8: Split-screen reading interface for uncertainty-aware document comprehension. a, Left panel displays source document [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: UncertaintyVis workflow for uncertainty-aware visualization generation. a, Researchers manually identify and highlight text excerpts containing quantitative data with uncertainty expressions across all four taxonomy categories from source documents. b, GPT-4 processes …
Figure 11
Figure 11. Figure 11: a, Single bar chart compares Text-to-Chart vs [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: a, Single bar chart compares answer accuracy be [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 14
Figure 14. Figure 14: Participant Preference for UncertaintyVis shows four questions with stacked distribution bars (1-7 scale) profile suggests the intervention operates on cognitive interpre￾tation processes rather than affecting global perceptions of task difficulty or interface quality…

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Works this paper leans on

52 extracted references · 45 canonical work pages

  1. [1]

    Satya Almasian, V. N. Kazakova, Philip Goldner, and Michael Gertz. 2023. CQE: A Comprehensive Quantity Extractor.ArXivabs/2305.08853 (2023). https: //api.semanticscholar.org/CorpusID:258686292

    work page Pith review arXiv 2023

  2. [2]

    Omar Alonso and Thibault Sellam. 2018. Quantitative Information Extraction From Social Data.The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval(2018). https://api.semanticscholar.org/ CorpusID:195351869

    work page 2018

  3. [3]

    Sarah Alsufyani, Matthew Forshaw, and Sara Johansson. 2024. Visualization of missing data: a state-of-the-art survey.ArXivabs/2410.03712 (2024). https: //api.semanticscholar.org/CorpusID:273186220

    arXiv 2024

  4. [4]

    Hearst, Andrew Head, and Kyle Lo

    Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, and Kyle Lo. 2022. Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing.ACM Transactions on Computer-Human Interaction30 (2022), 1 – 38. https://api.semanticscholar.org/ CorpusID:247187606

    2022

  5. [5]

    Alejandro Benito-Santos, Michelle Doran, Aleyda Rocha, Eveline Wandl-Vogt, Jennifer Edmond, and Roberto Therón. 2021. Evaluating a Taxonomy of Textual Uncertainty for Collaborative Visualisation in the Digital Humanities.Inf.12 (2021), 436. https://api.semanticscholar.org/CorpusID:239564394

    work page 2021

  6. [7]

    Nadia Boukhelifa, Anastasia Bezerianos, Tobias Isenberg, and Jean-Daniel Fekete

  7. [8]

    Castro, P

    Spencer C. Castro, P. Samuel Quinan, Helia Hosseinpour, and Lace M. K. Padilla

    work page

  8. [9]

    https://api.semanticscholar.org/CorpusID:16978566

    Evaluating Sketchy Lines for the Visualization of Qualitative Uncertainty. https://api.semanticscholar.org/CorpusID:16978566

    work page

Show all 52 references
  1. [10]

    Michael Correll, Dominik Moritz, and Jeffrey Heer. 2018. Value-Suppressing Uncertainty Palettes.Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems(2018). https://api.semanticscholar.org/CorpusID:3413551

    2018

  2. [11]

    Rosen, Gerbrand Ceder, Kristin A

    John Dagdelen, Alex Dunn, Sanghoon Lee, Nicholas Walker, Andrew S. Rosen, Gerbrand Ceder, Kristin A. Persson, and Anubhav Jain. 2024. Structured in- formation extraction from scientific text with large language models.Nature Communications15 (2024). https://api.semanticscholar...

    2024

  3. [12]

    Beane, Ting-Hao ’Kenneth’ Huang, Bryan R

    Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dy- lan Langdon, Reema Moussa, Matthew I. Beane, Ting-Hao ’Kenneth’ Huang, Bryan R. Routledge, and William Yang Wang. 2021. FinQA: A Dataset of Nu- merical Reasoning over Financial Data.ArXivabs/2109.00122 (202...

    2021 arXiv

  4. [13]

    Valentin Edelsbrunner, Jinrui Wang, Alexis Pister, Tomas Vancisin, Sian Phillips, Min Chen, and Benjamin Bach. 2025. Visualization Badges: Communicating Design and Provenance through Graphical Labels Alongside Visualizations. IEEE transactions on visualization and computer gra...

    2025

  5. [14]

    Luca Foppiano, Laurent Romary, Masashi Ishii, and Mikiko Tanifuji. 2019. Auto- matic Identification and Normalisation of Physical Measurements in Scientific Literature.Proceedings of the ACM Symposium on Document Engineering 2019 (2019). https://api.semanticscholar.org/CorpusI...

    2019

  6. [15]

    Dmitry Davidov and Ari Rappoport. 2010. Extraction and Approximation of Numerical Attributes from the Web. InAnnual Meeting of the Association for Computational Linguistics. https://api.semanticscholar.org/CorpusID:6381874

    2010

  7. [16]

    Miriam Greis, Aditi Joshi, Ken Singer, Albrecht Schmidt, and Tonja Machulla

  8. [17]

    Bakker, Stephen Li, Tim Kraska, and César A

    Kevin Zeng Hu, Michiel A. Bakker, Stephen Li, Tim Kraska, and César A. Hidalgo

  9. [18]

    Jan Göpfert, Patrick Kuckertz, Jann Michael Weinand, Leander Kotzur, and Detlef Stolten. 2022. Measurement Extraction with Natural Language Processing: A Review. InConference on Empirical Methods in Natural Language Processing. https://api.semanticscholar.org/CorpusID:256631031

    2022

  10. [19]

    Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, and Matthew Kay

    Jessica R. Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, and Matthew Kay

  11. [20]

    Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, and Jiawei Han. 2023. Instruct and Extract: Instruction Tuning for On-Demand Informa- tion Extraction.ArXivabs/2310.16040 (2023). https://api.semanticscholar.org/ CorpusID:264439524

    2023 arXiv

  12. [21]

    Joslyn and Sonia Savelli

    Susan L. Joslyn and Sonia Savelli. 2021. Visualizing Uncertainty for Non-Expert End Users: The Challenge of the Deterministic Construal Error. InFrontiers of Computer Science. https://api.semanticscholar.org/CorpusID:231714165

    2021

  13. [22]

    Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2018)

    VizML: A Machine Learning Approach to Visualization Recommendation. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (2018). https://api.semanticscholar.org/CorpusID:52012313

    2018

  14. [23]

    Jessica R. Hullman. 2019. Why Authors Don’t Visualize Uncertainty.IEEE Transactions on Visualization and Computer Graphics26 (2019), 130–139. https: //api.semanticscholar.org/CorpusID:199442322

    2019

  15. [24]

    Javaid, Vijay Kumar Devabhaktuni, Devinder Kaur, Jack Zaientz, and Robert P

    Aasim Kamal, Parashar Dhakal, Ahmad Y. Javaid, Vijay Kumar Devabhaktuni, Devinder Kaur, Jack Zaientz, and Robert P. Marinier. 2021. Recent advances and challenges in uncertainty visualization: a survey.Journal of Visualization24 (2021), 861 – 890. https://api.semanticscholar.o...

    2021

  16. [25]

    Isaak Kavasidis, Sergio Palazzo, Concetto Spampinato, Carmelo Pino, Daniela Giordano, Daniele Giuffrida, and Paolo Messina. 2018. A Saliency-based Con- volutional Neural Network for Table and Chart Detection in Digitized Docu- ments. InInternational Conference on Image Analysi...

    2018

  17. [26]

    Hullman, and Sean A Munson

    Matthew Kay, Tara Kola, Jessica R. Hullman, and Sean A Munson. 2016. When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems.Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems(2016). https://api.semant...

    2016

  18. [27]

    Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, and Huamin Qu. 2021. KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommenda- tion.IEEE Transactions on Visualization and Computer GraphicsPP (2021), 1–1. https://api.semanticscholar.org/CorpusID:236447420

    2021

  19. [28]

    Alex Kale, Matthew Kay, and Jessica R. Hullman. 2020. Visual Reasoning Strate- gies for Effect Size Judgments and Decisions.IEEE Transactions on Visualization and Computer Graphics27 (2020), 272–282. https://api.semanticscholar.org/ CorpusID:221662355

    2020

  20. [29]

    Alex Kale, Francis Nguyen, Matthew Kay, and Jessica R. Hullman. 2019. Hypothet- ical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data. IEEE Transactions on Visualization and Computer Graphics25 (2019), 892–902. https://api.semanticscholar.org/CorpusID:51776749

    2019

  21. [30]

    Aman Madaan, Ashish Mittal, Mausam, Ganesh Ramakrishnan, and Sunita Sarawagi. 2016. Numerical Relation Extraction with Minimal Supervision. InAAAI Conference on Artificial Intelligence. https://api.semanticscholar.org/ CorpusID:2427400

    2016

  22. [31]

    Damien Masson, Sylvain Malacria, Géry Casiez, and Daniel Vogel. 2023. Chara- graph: Interactive Generation of Charts for Realtime Annotation of Data-Rich Paragraphs.Proceedings of the 2023 CHI Conference on Human Factors in Com- puting Systems(2023). https://api.semanticschola...

    2023

  23. [32]

    Tamara Munzner. 2014. Visualization Analysis and Design.Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Courses(2014). https://api.semanticscholar.org/CorpusID:19076114

    2014

  24. [33]

    Lace Padilla, Matthew Kay, and Jessica Hullman. 2020. Uncertainty visualization. Computational Statistics in Data Science(2020)

    2020

  25. [34]

    Daniele Liberatore, Kyriaki Kalimeri, Derya Sever, and Yelena Mejova. 2024. Quantitative Information Extraction from Humanitarian Documents.Proceedings of the 2024 International Conference on Information Technology for Social Good (2024). https://api.semanticscholar.org/Corpus...

    2024

  26. [35]

    Maceachren, Robert E

    Alan M. Maceachren, Robert E. Roth, James O’Brien, Bonan Li, Derek Swingley, and Mark Gahegan. 2012. Visual Semiotics & Uncertainty Visualization: An Empirical Study.IEEE Transactions on Visualization and Computer Graphics18 (2012), 2496–2505. https://api.semanticscholar.org/C...

    2012

  27. [36]

    Szekely, and Filip Ilievski

    Avijit Thawani, Jay Pujara, Pedro A. Szekely, and Filip Ilievski. 2021. Representing Numbers in NLP: a Survey and a Vision.ArXivabs/2103.13136 (2021). https: //api.semanticscholar.org/CorpusID:232335764

    2021 arXiv

  28. [37]

    Liangwei Wang, Zhan Wang, Shishi Xiao, Le Liu, Fugee Tsung, and Wei Zeng

  29. [38]

    Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, and Yong Wang. 2023. LLM4Vis: Explainable Visualization Recommendation using ChatGPT. InCon- ference on Empirical Methods in Natural Language Processing. https://api. semanticscholar.org/CorpusID:263834585

    2023

  30. [39]

    Jo Wood, Petra Isenberg, Tobias Isenberg, Jason Dykes, Nadia Boukhelifa, and Aidan Slingsby. 2012. Sketchy Rendering for Information Visualization.IEEE Transactions on Visualization and Computer Graphics18 (2012), 2749–2758. https: //api.semanticscholar.org/CorpusID:7547714

    2012

  31. [40]

    Hayeong Song and Danielle Albers Szafir. 2019. Where’s My Data? Evaluating Vi- sualizations with Missing Data.IEEE Transactions on Visualization and Computer Graphics25 (2019), 914–924. https://api.semanticscholar.org/CorpusID:52068061

    2019

  32. [41]

    Allen Daniel Sunny. 2025. Trust in Transparency: How Explainable AI Shapes User Perceptions.ArXivabs/2510.04968 (2025). https://api.semanticscholar.org/ CorpusID:281843686

    2025

  33. [42]

    Mengyu Zhou, Qingtao Li, Xinyi He, Yuejiang Li, Yibo Liu, Wei Ji, Shi Han, Yining Chen, Daxin Jiang, and Dongmei Zhang. 2020. Table2Charts: Recommending Charts by Learning Shared Table Representations.Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data ...

    2020

  34. [43]

    Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat seng Chua. 2021. TAT-QA: A Question An- swering Benchmark on a Hybrid of Tabular and Textual Content in Finance. InAnnual Meeting of the Association for Computational Linguistics...

    2021

  35. [44]

    Sujia Zhu, Guodao Sun, Qi Jiang, Meng Zha, and Ronghua Liang. 2020. A survey on automatic infographics and visualization recommendations.Visual Informatics 4, 3 (2020), 24–40

    2020

  36. [45]

    chart_type

    Ruishi Zou, Yinqi Tang, Jingzhu Chen, Siyu Lu, Yan Lu, Yingfan Yang, and Chen Ye. 2025. GistVis: Automatic Generation of Word-scale Visualizations from Data- rich Documents.Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems(2025). https://api.semantic...

    2025

  37. [47]

    Songheng Zhang, Lei Wang, Toby Jia-Jun Li, Qiaomu Shen, Yixin Cao, and Yong Wang. 2024. ChartifyText: Automated Chart Generation from Data-Involved 27 Zhang et al. Texts via LLM.ArXivabs/2410.14331 (2024). https://api.semanticscholar.org/ CorpusID:273482078

    2024 arXiv

  38. [48]

    Wang, Haotian Li, and Huamin Qu

    Songheng Zhang, Y. Wang, Haotian Li, and Huamin Qu. 2023. AdaVis: Adap- tive and Explainable Visualization Recommendation for Tabular Data.IEEE Transactions on Visualization and Computer Graphics30 (2023), 5923–5938. https://api.semanticscholar.org/CorpusID:262055731

    2023

  39. [2012]

    https://api.semanticscholar.org/CorpusID:14121435

    Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty.IEEE Transactions on Visualization and Computer Graphics18 (2012), 2769–2778. https://api.semanticscholar.org/CorpusID:14121435

    2012

  40. [2018]

    https://api.semanticscholar.org/CorpusID:5051169

    Uncertainty Visualization Influences how Humans Aggregate Discrepant Information.Proceedings of the 2018 CHI Conference on Human Factors in Com- puting Systems(2018). https://api.semanticscholar.org/CorpusID:5051169

    2018

  41. [2019]

    IEEE Transactions on Visualization and Computer Graphics25 (2019), 903–913

    In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation. IEEE Transactions on Visualization and Computer Graphics25 (2019), 903–913. https://api.semanticscholar.org/CorpusID:51920450

    2019

  42. [2021]

    https://api.semanticscholar.org/CorpusID: 238229978

    Examining Effort in 1D Uncertainty Communication Using Individual Dif- ferences in Working Memory and NASA-TLX.IEEE Transactions on Visualization and Computer GraphicsPP (2021), 1–1. https://api.semanticscholar.org/CorpusID: 238229978

    2021

  43. [2023]

    ” Uncertainty Categories:Precision BoundariesChart Type:Vertical Bar Chart Select the chart that best represents the uncertainty in the source text: Uncertainty Visualization Alternative Visualization Certainty Visualization34 UncertaintyVis: Preserving Linguistic Uncertainty ...

    1970

  44. [2025]

    https://api.semanticscholar.org/CorpusID:277955116

    VizTA: Enhancing Comprehension of Distributional Visualization with Visual-Lexical Fused Conversational Interface.Computer Graphics Forum44 (2025). https://api.semanticscholar.org/CorpusID:277955116

    2025

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.