A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions
Bibliographic record
Abstract
Investment management professionals (IMPs) often make decisions after manual analysis of text transcripts of central banks' conferences or companies' earning calls. Their current software tools, while interactive, largely leave users unassisted in using these transcripts. A key component to designing speech and NLP techniques for this community is to qualitatively characterize their perceptions of AI as well as their legitimate needs so as to (1) better apply existing NLP methods, (2) direct future research and (3) correct IMPs' perceptions of what AI is capable of. This paper presents such a study, through a contextual inquiry with eleven IMPs, uncovering their information practices when using such transcripts. We then propose a taxonomy of user requirements and usability criteria to support IMP decision making, and validate the taxonomy through participatory design workshops with four IMPs. Our investigation suggests that: (1) IMPs view visualization methods and natural language processing algorithms primarily as time-saving tools that are incapable of enhancing either discovery or interpretation and (2) their existing software falls well short of the state of the art in both visualization and NLP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".