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REVIEW 3 major objections 5 minor 56 references

Visual Text Mining with Progressive Taxonomy Construction for Environmental Studies

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read GreenMine lets environmental experts progressively build a DPSIR taxonomy from interview transcripts through a three-step LLM prompting pipeline with consistency-based uncertainty to guide refinement.

desk verdict A solid, honestly limited design study: the integrated system is the contribution, and the unvalidated uncertainty metric is a real gap but not a fatal one. read the letter →

arxiv 2502.05731 v2 pith:Z4XMESCO submitted 2025-02-09 cs.HC

Sam Yu-Te Lee , Cheng-Wei Hung , Mei-Hua Yuan , Kwan-Liu Ma This is my paper · ORCID
classification cs.HC
keywords DPSIRframeworklargelanguagemodelspromptengineeringtextmininguncertaintyestimationvisualanalyticstaxonomyconstructionhuman-in-the-loop
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

GreenMine is a visual analytics system that lets environmental experts mine DPSIR relationships from interview transcripts without a fixed, pre-made label set. The paper's central claim is that the DPSIR taxonomy can be constructed progressively: experts start with abstract indicator definitions, and a three-step prompting pipeline (indicator identification, variable identification, link identification) converts each mining task into a multi-label classification whose results experts can inspect and refine. The load-bearing evaluation device is a consistency-based uncertainty score: each prompt is run multiple times, and the average pairwise Jaccard distance between the responses flags snippets that do not fit the current definitions. A case study on transcripts from Lyudao shows experts adding variables and refining definitions based on high-uncertainty outliers, and the authors argue the same human-in-the-loop pattern could generalize to other knowledge-intensive text mining tasks.

What carries the argument

The central mechanism is the three-step prompting pipeline backed by a consistency-based uncertainty chart. The pipeline first asks the LLM to identify which of the five DPSIR indicators appear in each snippet, then which expert-defined variables under those indicators appear, and finally whether each pair of variables is linked, with the LLM supplying evidence sentences and explanations at every step. The uncertainty score $D_i$ is the average pairwise Jaccard distance across $k$ repeated LLM responses to the same prompt, implemented by re-running prompts with temperature zero. The chart maps each snippet to a polar coordinate whose angle encodes the semantic similarity of its evidence embeddings and whose radius encodes this uncertainty, so semantically related snippets form clusters and uncertain snippets sit at the periphery. This design is what allows experts to interleave topic exploration with evaluation of whether the current taxonomy fits the corpus.

What would settle it

One could take a set of snippets with human-annotated DPSIR labels, run the pipeline with the initial taxonomy, and correlate each snippet's uncertainty score ($D_i$ from Equation 1) with a human judge's rating of whether the snippet fits the taxonomy. If the correlation is near zero, or if refining only the high-uncertainty snippets fails to reduce labeling error more than refining a random subset, the central evaluation loop would not be doing the work the paper claims.

Watch

Extended reading notes

Core claim

The paper demonstrates that a domain-specific taxonomy like DPSIR can be built up interactively with LLM assistance rather than being fixed in advance. It decomposes the mining into three sequential subtasks, each formulated as a multi-label classification so that a repeated-sampling consistency measure gives an interpretable uncertainty value. The authors report that even with temperature set to zero, uncertainty scores can reach 0.8, and they treat such inconsistencies as signals that the current taxonomy definition is incomplete or misaligned. In the case study, a high-uncertainty snippet about a 'Little Vendor Dream' led the expert to add 'improving living conditions' to the Response definition, and a keyword cloud over a 'miscellaneous' label led to adding 'culture security' and 'garbage' as Driver variables. The claim is that this cycle of explore, evaluate, and refine produces a contextualized taxonomy and mined relationships that would otherwise require laborious manual annotation.

Load-bearing premise

The system's refinement loop works only if a high consistency-based uncertainty score usually means 'this snippet does not fit the current taxonomy definition' rather than 'the model is randomly flipping labels'; the paper does not validate this mapping against ground-truth labels or human coding.

Editorial extensions

If this is right

  • Experts can build a contextualized DPSIR taxonomy from a real interview corpus without a pre-existing validation set, refining definitions as they read.
  • Each subtask of the pipeline (indicator, variable, link) is a separate multi-label classification, so failures can be localized and corrected without re-prompting the whole workflow.
  • The uncertainty chart lets experts use the same view to find missing definitions (peripheral uncertain snippets) and to check that confident snippets match their intent (central certain snippets).
  • The DPSIR Graph aggregates mined variables and links into a radial diagram that supports collaborative discussion and policymaking communication with progressive disclosure.
  • Because the prompting templates expose only the domain-definition parts, non-technical experts can operate the system without writing raw prompts.

Reading between the lines

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

  • If the uncertainty-as-mismatch assumption holds beyond DPSIR, the same radial chart could serve as a generic diagnostic for any LLM labeling pipeline, flagging corpus segments where a codebook is incomplete.
  • The 'miscellaneous' variable plus keyword cloud suggests a concrete quantitative extension: measuring how many new taxonomy variables are discovered per refinement cycle, and whether recall gains justify the added prompts.
  • The paper's pipeline could transfer to thematic analysis codebooks, but a test would need to confirm that the three-step decomposition (concept, tag, link) remains sensible when the target is open-ended themes rather than five fixed DPSIR indicators.
  • One could test the system's recall-over-precision commitment by comparing precision and recall against human coding on a held-out set; the paper does not report such numbers.
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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

3 major / 5 minor

Summary. This paper presents GreenMine, a visual analytics system for human-in-the-loop text mining with the DPSIR environmental framework. The system implements a three-step LLM prompting pipeline (indicator, variable, and link identification) in which the taxonomy, expressed in natural language, can be iteratively refined. To guide refinement, the system computes an uncertainty score as the average pairwise Jaccard distance over k repeated prompt executions (Eq. 1) and visualizes this uncertainty in a radial chart whose angular coordinate encodes topic similarity. A DPSIR graph supports collaborative discussion of mined relationships. The authors report a case study with one environmental expert (E1) on 598 interview snippets from Lyudao, and an expert review session with three additional experts. They claim the system supports progressive taxonomy construction and insight discovery, and they draw lessons for other knowledge-intensive tasks.

Significance. The paper integrates prompt engineering, uncertainty estimation, and radial visualization in a plausible workflow for progressive taxonomy construction. Its strengths include the decomposition of the mining task into three evaluable subtasks, the combination of uncertainty and topic semantics in one chart, and the transparency about the evaluation's limitations, alongside a public code repository. However, the central mechanism—that consistency-based uncertainty flags incomplete taxonomy definitions—remains unvalidated, and the evaluation evidence is anecdotal and largely self-referential. If the uncertainty signal were validated, this would be a useful contribution to visual analytics for LLM-based text mining; as it stands, the effectiveness claim is not yet established.

major comments (3)
  1. [§5.2, Eq. (1)] The uncertainty score is the load-bearing signal for the progressive refinement loop, but the paper does not demonstrate that high Jaccard inconsistency corresponds to definitional gaps in the taxonomy rather than random model noise. The temperature is set to 0, so residual inconsistency arises from non-deterministic inference; the two anecdotes in Section 6 could be cherry-picked by chance. The paper provides no validation of the uncertainty measure against ground truth (e.g., a small human-coded sample), no analysis of whether refinements reduced uncertainty on the originally problematic snippets, and no measurement of whether refinements improved mining quality (precision or recall). Without this, the interleaved evaluation-exploration loop (R2, §5.3) has no demonstrated benefit in terms of mining accuracy.
  2. [§7 and §4] The evaluation is substantially self-referential. E1 and E2, who are co-authors, helped design the system and conducted the case study; the expert review was led by E1, who presented findings and used a tutorial prepared by the authors. The three new experts (E3–E5) had not seen the system before, but the session was a free-form discussion rather than a structured evaluation. The paper acknowledges this in Section 8, but the claims about 'effectiveness' and 'progressive taxonomy construction' would require at least a semi-structured evaluation with independent experts, or a quantitative comparison. As it stands, the results show usability and perceived value, not that the system's uncertainty-driven refinement improves over alternative workflows.
  3. [Section 6] The case study is a single corpus of 598 snippets, and no quantitative measure of mining accuracy is reported. Although R3 deliberately prioritizes recall over precision, the paper does not even report a rough estimate of recall (e.g., how many snippets with a given variable were missed by the pipeline). The examples of 'Little Vendor Dream' and 'garbage' are suggestive, but they do not demonstrate that the pipeline extracted the majority of relevant DPSIR content. Without any precision/recall numbers or a comparison baseline, the claim that GreenMine 'supports interactive mining of DPSIR relationships' is supported only by narrative.
minor comments (5)
  1. [§5.5 heading] The heading 'Progressie Disclosure' should be 'Progressive Disclosure'.
  2. [Section 6] The text refers to the 'White Error' historical period; the intended term is presumably 'White Terror'.
  3. [§5.2] The statement that temperature is set to 0 'for maximal determinism' is immediately qualified by 'Still, uncertainty scores can reach 0.8'; it would be clearer to say that temperature reduces but does not eliminate nondeterminism, and to explain the likely sources of residual inconsistency (e.g., batching or sampling implementation).
  4. [References] Reference [49] gives the author name as 'Zamifirescu'; this should be 'Zamfirescu'.
  5. [§5.2] The claim that the bottleneck 'is simply Network I/O' and that OpenAI's clusters 'likely' have enough resources is stronger than what is demonstrated; consider softening the wording.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: GreenMine's uncertainty score and prompting pipeline are self-contained; the acknowledged self-referential expert evaluation is an external-validity limitation, not a derivation-level circularity.

full rationale

The claimed derivation chain (the three-step prompting pipeline, consistency-based uncertainty score, uncertainty chart, and DPSIR graph) does not reduce any output to its input by construction. The uncertainty score in Eq. 1 is a computed statistic over k repeated LLM responses, explicitly following Chen and Mueller [8]; it is not fitted to the target concept of taxonomy incompleteness. Using it to prioritize snippets for expert inspection is a heuristic whose validity would need external ground-truth testing (a correctness risk), not a circular definition. The pipeline steps in Sec. 5.1 take the expert-supplied DPSIR taxonomy as input and return extractions; the case-study refinements are human judgments made by E1, so no prediction is obtained by renaming a fitted parameter. Equation 2 is a standard circular-MDS embedding objective independent of the taxonomy claim. Self-citations ([22], [50], [51]) appear only as motivational context and are not load-bearing. The paper itself flags in Sec. 8 that evaluation is based solely on feedback from a small group of collaborating experts and that the experts received a detailed tutorial from the authors; combined with E1/E2 being co-authors, this limits generalizability but is an evaluation-validity limitation, not a circular derivation. No equation or fitted value is reused as a predicted outcome, so no formal circularity is present.

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

The system operates entirely with existing constructs: DPSIR indicators, variables, LLM responses, embeddings, and Jaccard distances. No new physical or ontological entities are introduced. The central claim rests mainly on the assumption that repeated-response inconsistency is a valid proxy for taxonomy gaps, and on the fixed choice of LLM and embedding models.

free parameters (4)
  • k (number of repeated LLM samples per prompt) = 5
    Chosen for the uncertainty estimate; the paper states uncertainty calculation adds 1.5x overhead when k=5. The choice affects Jaccard uncertainty values and chart radius encoding.
  • agglomerative clustering threshold = 0.5 (cosine similarity)
    Used to define topics in the uncertainty chart; a hand-set value that influences cluster assignment and topic labels.
  • GPT temperature = 0
    Set for maximal determinism, yet inconsistencies still occur; this choice shapes the uncertainty scores.
  • model choices = gpt-4o-mini and text-embedding-3-small
    The LLM and embedding models are fixed choices; the paper notes it is untested with other models.
assumptions (4)
  • domain assumption The DPSIR framework is a valid and appropriate structure for organizing the environmental study of Lyudao
    Brought in from the start of Section 2; the entire system targets DPSIR taxonomy construction, and no validation of DPSIR applicability is offered.
  • ad hoc to paper Response inconsistency across repeated LLM runs is a meaningful proxy for taxonomy incompleteness
    Equation 1 defines uncertainty as Jaccard distance; the paper assumes high uncertainty pinpoints definitional gaps, which underpins the evaluation loop in Section 5.2 and the case study in Section 6.
  • domain assumption The interview transcripts and the expert's preliminary taxonomy are a suitable testbed
    Section 4 and Section 6 rely on 19 transcripts and the initial taxonomy derived from literature; representativeness is assumed.
  • domain assumption An LLM can reliably perform multi-label classification of indicators, variables, and links from prompt templates
    The whole pipeline relies on gpt-4o-mini following the templates in Figure 2; no ground-truth evaluation of extraction quality is provided.

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Cite this review

Pith. "Pith review of Visual Text Mining with Progressive Taxonomy Construction for Environmental Studies." pith.science (2026). https://pith.science/paper/Z4XMESCO

@misc{pith2026250205731,
  author       = {Pith},
  title        = {Pith review of: Visual Text Mining with Progressive Taxonomy Construction for Environmental Studies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z4XMESCO}},
  note         = {Machine review of arXiv:2502.05731}
}
read the original abstract

Environmental experts have developed the DPSIR (Driver, Pressure, State, Impact, Response) framework to systematically study and communicate key relationships between society and the environment. Using this framework requires experts to construct a DPSIR taxonomy from a corpus, annotate the documents, and identify DPSIR variables and relationships, which is laborious and inflexible. Automating it with conventional text mining faces technical challenges, primarily because the taxonomy often begins with abstract definitions, which experts progressively refine and contextualize as they annotate the corpus. In response, we develop GreenMine, a system that supports interactive text mining with prompt engineering. The system implements a prompting pipeline consisting of three simple and evaluable subtasks. In each subtask, the DPSIR taxonomy can be defined in natural language and iteratively refined as experts analyze the corpus. To support users evaluate the taxonomy, we introduce an uncertainty score based on response consistency. Then, we design a radial uncertainty chart that visualizes uncertainties and corpus topics, which supports interleaved evaluation and exploration. Using the system, experts can progressively construct the DPSIR taxonomy and annotate the corpus with LLMs. Using real-world interview transcripts, we present a case study to demonstrate the capability of the system in supporting interactive mining of DPSIR relationships, and an expert review in the form of collaborative discussion to understand the potential and limitations of the system. We discuss the lessons learned from developing the system and future opportunities for supporting interactive text mining in knowledge-intensive tasks for other application scenarios.

Figures

Figures reproduced from arXiv: 2502.05731 by the authors.

Figure 1
Figure 1. Example of a DPSIR diagram from Atkins et al. [4]. The [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The system design of GreenMine. Starting with a corpus, the documents are first segmented into snippets. Then, the snippets go through an interactive prompting pipeline consisting of three steps: Indicator, Variable, and Link Identification. In each step, the environmental experts can use the uncertainty chart to explore the corpus, evaluate the prompting performances, and refine the DPSIR taxonomy. After the mining… view at source ↗
Figure 3
Figure 3. The process of generating the uncertainty chart. The dots are positioned using polar coordinates. The angles encode semantic [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The prompting interface consists of three panels: (a) The Entry Panel, where users can adjust DPSIR taxonomy definitions and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Left: Keyword cloud designed to support the exploration of “miscellaneous” variables. The keyword cloud combines embeddings and dimensionality reduction to position semantically similar keywords closer. It shows that “garbage” and “environment” are two significant keyw…
Figure 6
Figure 6. Figure 6: An uncertainty chart generated in the case study. Each [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Left: The DPSIR Graph shows the aggregated mining results in a progressive graph. The design follows the typical DPSIR diagram in environmental studies. The Driver block is highlighted with ingoing and outgoing links colored by the source of the link. Right: The States…

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Reference graph

Works this paper leans on

56 extracted references · 19 canonical work pages

  1. [1]

    Accessed: 2024-02-12

    ChatGPT.https://chatgpt.com. Accessed: 2024-02-12

    work page 2024

  2. [2]

    Accessed: 2024-02-12

    Claude.https://claude.ai/. Accessed: 2024-02-12

    work page 2024

  3. [3]

    https://www.promptingguide.ai/

    Prompt Engineering guide. https://www.promptingguide.ai/. Accessed: 2024-02-12

    work page 2024

  4. [4]

    J. P. Atkins, D. Burdon, M. Elliott, and A. J. Gregory. Management of the marine environment: Integrating ecosystem services and societal benefits with the DPSIR framework in a systems approach.Marine Pollution Bulletin, 62(2):215–226, 2011. doi: 10.1016/j.marpolbul. 2010.12.012

    doi:10.1016/j.marpolbul 2011

  5. [5]

    Strategic management analysis: from data to strategy diagram by LLM

    R. Brath, A. Bradley, and D. Jonker. Strategic management analysis: from data to strategy diagram by LLM, 2024. doi: 10.48550/arXiv. 2409.06643

    work page Pith review arXiv doi:10.48550/arxiv.2409.06643 2024

  6. [6]

    N. Cao, J. Sun, Y .-R. Lin, D. Gotz, S. Liu, and H. Qu. FacetAtlas: Multifaceted visualization for rich text corpora.IEEE Transactions on Visualization and Computer Graphics, 16(6):1172–1181, 2010. doi: 10 .1109/TVCG.2010.154

    work page 2010

  7. [7]

    Chang, X

    Y . Chang, X. Wang, J. Wang, Y . Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y . Wang, W. Ye, Y . Zhang, Y . Chang, P. S. Yu, Q. Yang, and X. Xie. A survey on evaluation of large language models.ACM Trans. Intell. Syst. Technol., 15(3), 2024. doi: 10.1145/3641289

    doi:10.1145/3641289 2024

  8. [8]

    Chen and J

    J. Chen and J. Mueller. Quantifying uncertainty in answers from any language model and enhancing their trustworthiness. In L.-W. Ku, A. Martins, and V . Srikumar, eds.,Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5186–5200, 2024. doi: 10.18653/v1/2024.acl-long. 283

    doi:10.18653/v1/2024.acl-long 2024

Show all 56 references
  1. [9]

    Cheng, V

    F. Cheng, V . Zouhar, S. Arora, M. Sachan, H. Strobelt, and M. El- Assady. Relic: Investigating large language model responses using self- consistency. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24), 2024. doi: 10.1145/3613904. 3641904

    2024 doi

  2. [10]

    Dagdelen, A

    J. Dagdelen, A. Dunn, S. Lee, N. Walker, A. S. Rosen, G. Ceder, K. A. Persson, and A. Jain. Structured information extraction from scientific text with large language models.Nature Communications, 15(1):1418,

  3. [11]

    S.-C. Dai, A. Xiong, and L.-W. Ku. Llm-in-the-loop: Leveraging large language model for thematic analysis, 2023. doi: 10.48550/arXiv.2310 .15100

    2023 doi

  4. [12]

    Douglas Carroll and P

    J. Douglas Carroll and P. Arabie. Chapter 3 - Multidimensional Scaling. In M. H. Birnbaum, ed.,Measurement, Judgment and Decision Making, Handbook of Perception and Cognition (Second Edition), pp. 179–250. Academic Press, 1998. doi: 10.1016/B978-012099975-0.50005-1

    1998 doi

  5. [13]

    Fereday and E

    J. Fereday and E. Muir-Cochrane. Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development.International Journal of Qualitative Methods, 5(1):80–92, 2006. doi: 10.1177/160940690600500107

    2006 doi

  6. [14]

    J. R. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd Annual Meeting on Association for Computa- tional Linguistics (ACL ’05), p. 363–370, 2005. doi: 10.3115/1219840. 1219885

    2005 doi

  7. [15]

    T. M. J. Fruchterman and E. M. Reingold. Graph drawing by force- directed placement.Software: Practice and Experience, 21(11):1129– 1164, 1991. doi: 10.1002/spe.4380211102

    1991 doi

  8. [16]

    L. Gao, X. Ma, J. Lin, and J. Callan. Precise zero-shot dense retrieval without relevance labels, 2022. doi: 10.48550/arXiv.2212.10496

    2022 doi arXiv

  9. [17]

    Harvel, F

    N. Harvel, F. B. Haiek, A. Ankolekar, and D. J. Brunner. Can llms answer investment banking questions? using domain-tuned functions to improve llm performance on knowledge-intensive analytical tasks. Proceedings of the AAAI Symposium Series, 3(1):125–133, 2024. doi: 10.1609/aa...

    2024 doi

  10. [18]

    P. Jaccard. The distribution of the flora in the alpine zone.New Phy- tologist, 11(2):37–50, 1912. doi: 10.1111/j.1469-8137.1912.tb05611. x

    1912

  11. [19]

    T. Khot, H. Trivedi, M. Finlayson, Y . Fu, K. Richardson, P. Clark, and A. Sabharwal. Decomposed prompting: A modular approach for solving complex tasks, 2023. doi: 10.48550/arXiv.2210.02406

    2023 doi arXiv

  12. [20]

    T. S. Kim, Y . Lee, J. Shin, Y .-H. Kim, and J. Kim. EvalLM: Interactive evaluation of large language model prompts on user-defined criteria. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24), 2024. doi: 10.1145/3613904.3642216

    2024

  13. [21]

    L. Kuhn, Y . Gal, and S. Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,

  14. [22]

    S. Y .-T. Lee, A. Bahukhandi, D. Liu, and K.-L. Ma. Towards dataset- scale and feature-oriented evaluation of text summarization in large language model prompts.IEEE Transactions on Visualization and Com- puter Graphics, pp. 1–11, 2024. doi: 10.1109/TVCG.2024.3456398

    2024

  15. [23]

    T. Y . Lee, A. Smith, K. Seppi, N. Elmqvist, J. Boyd-Graber, and L. Find- later. The human touch: How non-expert users perceive, interpret, and fix topic models.International Journal of Human-Computer Studies, 105:28–42, 2017. doi: 10.1016/j.ijhcs.2017.03.007

    2017 doi

  16. [24]

    Lewis, E

    P. Lewis, E. Perez, A. Piktus, F. Petroni, V . Karpukhin, N. Goyal, H. K ¨uttler, M. Lewis, W. tau Yih, T. Rockt ¨aschel, S. Riedel, and D. Kiela. Retrieval-augmented generation for knowledge-intensive NLP tasks, 2021. doi: 10.48550/arXiv.2005.11401

    2021 doi arXiv

  17. [25]

    Q. V . Liao, D. Gruen, and S. Miller. Questioning the AI: Informing design practices for explainable AI user experiences. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20), p. 1–15, 2020. doi: 10.1145/3313831.3376590

    2020

  18. [26]

    D. C. Liu and J. Nocedal. On the limited memory BFGS method for large scale optimization.Mathematical programming, 45(1):503–528,

  19. [27]

    H. Liu, H. Yin, Z. Luo, and X. Wang. Integrating chemistry knowledge in large language models via prompt engineering.Synthetic and Sys- tems Biotechnology, 10(1):23–38, 2025. doi: 10.1016/j.synbio.2024. 07.004

    2025 doi

  20. [28]

    P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig. Pre-train, Prompt, and Predict: A systematic survey of prompting methods in natural language processing.ACM Comput. Surv., 55(9), 2023. doi: 10. 1145/3560815

    2023

  21. [29]

    S. Liu, X. Wang, C. Collins, W. Dou, F. Ouyang, M. El-Assady, L. Jiang, and D. A. Keim. Bridging text visualization and mining: A task-driven survey.IEEE Transactions on Visualization and Com- puter Graphics, 25(7):2482–2504, 2019. doi: 10.1109/TVCG.2018. 2834341

    2019 doi

  22. [30]

    MacNeil, A

    S. MacNeil, A. Tran, J. Kim, Z. Huang, S. Bernstein, and D. Mogil. Prompt Middleware: Mapping prompts for large language models to UI affordances, 2023. doi: 10.48550/arXiv.2307.01142

    2023 doi arXiv

  23. [31]

    Malik, S

    U. Malik, S. Bernard, A. Pauchet, C. Chatelain, R. Picot-Cl ´emente, and J. Cortinovis. Pseudo-labeling with large language models for multi-label emotion classification of French tweets.IEEE Access, 12:15902–15916, 2024. doi: 10.1109/ACCESS.2024.3354705

    2024

  24. [32]

    S. C. Mangi, C. M. Roberts, and L. D. Rodwell. Reef fish- eries management in kenya: Preliminary approach using the driver–pressure–state–impacts–response (DPSIR) scheme of indicators. Ocean & Coastal Management, 50(5):463–480, 2007. doi: 10.1016/j. ocecoaman.2006.10.003

    2007 doi

  25. [33]

    P. N. Mendes, M. Jakob, A. Garc ´ıa-Silva, and C. Bizer. DBpedia spotlight: shedding light on the web of documents. InProceedings of the 7th International Conference on Semantic Systems (I-Semantics ’11), p. 1–8, 2011. doi: 10.1145/2063518.2063519

    2011

  26. [34]

    Oesterwind, A

    D. Oesterwind, A. Rau, and A. Zaiko. Drivers and pressures – untan- gling the terms commonly used in marine science and policy.Journal of Environmental Management, 181:8–15, 2016. doi: 10.1016/j.jenvman. 2016.05.058

    2016 doi

  27. [35]

    D. Park, S. Kim, J. Lee, J. Choo, N. Diakopoulos, and N. Elmqvist. ConceptVector: Text visual analytics via interactive lexicon build- ing using word embedding.IEEE Transactions on Visualization and Computer Graphics, 24(1):361–370, 2018. doi: 10.1109/TVCG.2017. 2744478 10 © 2...

    2018

  28. [36]

    Sch¨olkopf, A

    B. Sch¨olkopf, A. Smola, and K.-R. M¨uller. Kernel principal component analysis. In W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, eds.,Artificial Neural Networks — ICANN’97, pp. 583–588. Springer Berlin Heidelberg, 1997. doi: 10.1007/BFb0020217

    1997 doi

  29. [37]

    C. E. Shannon. A mathematical theory of communication.The Bell System Technical Journal, 27(3):379–423, 1948. doi: 10.1002/j.1538 -7305.1948.tb01338.x

    1948

  30. [38]

    Steinbach, G

    M. Steinbach, G. Karypis, and V . Kumar. A comparison of docu- ment clustering techniques. Technical Report 00-034, University of Minnesota Twin Cities, 2000. doi: 11299/215421

    2000

  31. [39]

    van der Maaten and G

    L. van der Maaten and G. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86):2579–2605, 2008

    2008

  32. [40]

    M. Wan, T. Safavi, S. K. Jauhar, Y . Kim, S. Counts, J. Neville, S. Suri, C. Shah, R. W. White, L. Yang, R. Andersen, G. Buscher, D. Joshi, and N. Rangan. TnT-LLM: Text mining at scale with large language models. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Dis...

  33. [41]

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou. Chain-of-Thought prompting elicits reasoning in large language models, 2023. doi: 10.48550/arXiv.2201.11903

    2023 doi arXiv

  34. [42]

    Weidinger, J

    L. Weidinger, J. Uesato, M. Rauh, C. Griffin, P.-S. Huang, J. Mellor, A. Glaese, M. Cheng, B. Balle, A. Kasirzadeh, C. Biles, S. Brown, Z. Kenton, W. Hawkins, T. Stepleton, A. Birhane, L. A. Hendricks, L. Rimell, W. Isaac, J. Haas, S. Legassick, G. Irving, and I. Gabriel. Taxo...

    2022

  35. [43]

    D. Wong. VOSviewer.Technical Services Quarterly, 35(2):219–220,

  36. [44]

    doi: 10.1145/3637528.3671647

  37. [45]

    D. Xu, W. Chen, W. Peng, C. Zhang, T. Xu, X. Zhao, X. Wu, Y . Zheng, Y . Wang, and E. Chen. Large language models for generative informa- tion extraction: A survey, 2024. doi: 10.48550/arXiv.2312.17617

    2024 doi arXiv

  38. [46]

    J. Xu, Y . Tao, and H. Lin. Semantic word cloud generation based on word embeddings. InProceedings of the 2016 IEEE Pacific Visu- alization Symposium (PacificVis), pp. 239–243, 2016. doi: 10.1109/ PACIFICVIS.2016.7465278

    2016

  39. [47]

    L. Yan, V . Echeverria, G. M. Fernandez-Nieto, Y . Jin, Z. Swiecki, L. Zhao, D. Gaˇsevi´c, and R. Martinez-Maldonado. Human-ai collab- oration in thematic analysis using chatgpt: A user study and design recommendations. InExtended Abstracts of the CHI Conference on Human Facto...

    2024

  40. [48]

    S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y . Cao, and K. Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023. doi: 10.48550/arXiv.2305.10601

    2023 doi arXiv

  41. [49]

    Xiao and W

    Y . Xiao and W. Y . Wang. Quantifying uncertainties in natural lan- guage processing tasks.Proceedings of the AAAI Conference on Artifi- cial Intelligence, 33(01):7322–7329, 2019. doi: 10.1609/aaai.v33i01. 33017322

    2019 doi

  42. [50]

    Zhang, S

    X. Zhang, S. Chandrasegaran, and K.-L. Ma. ConceptScope: Or- ganizing and visualizing knowledge in documents based on domain ontology. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), 2021. doi: 10.1145/3411764. 3445396

    2021 doi

  43. [51]

    Zhang, J

    X. Zhang, J. Li, P.-W. Chi, S. Chandrasegaran, and K.-L. Ma. Con- ceptEV A: Concept-based interactive exploration and customization of document summaries. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), 2023. doi: 10. 1145/3544548.3581260 11

    2023

  44. [54]

    Zamfirescu-Pereira, R

    J. Zamfirescu-Pereira, R. Y . Wong, B. Hartmann, and Q. Yang. Why Johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), 2023. doi: 10.1145/3544548. 3581388

    2023 doi

  45. [1989]

    doi: 10.1007/BF01589116

    doi

  46. [2018]

    doi: 10.1080/07317131.2018.1425352

    2018

  47. [2023]

    doi: 10.48550/arXiv.2302.09664

    doi arXiv

  48. [2024]

    doi: 10.1038/s41467-024-45563-x

    doi

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.