| { |
| "id": "ml17-root", |
| "requirements": "A credible experiment comparing LDA, NMF, and LSA topic models on small 20newsgroups subsets: conditions are implemented, execution reports coherence and ARI on multiple subsets with repeated seeds, and results address H1/H2/H3 directionally.", |
| "judging_note": "Score on scientific substance and directional correctness of evidence, not on exact numeric thresholds. Alternative topic-model implementations or coherence approximations that test the same scientific question should be credited.", |
| "weight": 1, |
| "sub_tasks": [ |
| { |
| "id": "ml17-code", |
| "requirements": "Topic-model conditions and dataset pipeline are implemented correctly.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml17-code-models", |
| "requirements": "The submission implements LDA, NMF, and LSA (or equivalents such as a truncated SVD for LSA) as distinct modeling code paths with a matched topic-count setting.", |
| "weight": 12.5, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Method Implementation" |
| }, |
| { |
| "id": "ml17-code-data", |
| "requirements": "The submission loads multiple 20newsgroups subsets (or comparable document corpora) with consistent text preprocessing/vectorization documented in code.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Dataset and Model Acquisition" |
| }, |
| { |
| "id": "ml17-code-metrics-impl", |
| "requirements": "The code computes a coherence-style metric (e.g., c_v or a documented approximation) and ARI from document-cluster assignments for each method.", |
| "weight": 6.25, |
| "sub_tasks": [], |
| "task_category": "Code Development", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml17-exec", |
| "requirements": "Execution produces comparable numeric outputs for all implemented methods.", |
| "weight": 2, |
| "sub_tasks": [ |
| { |
| "id": "ml17-exec-runs", |
| "requirements": "Execution evaluates the core methods on multiple datasets and writes per-method, per-dataset coherence and ARI values to a machine-readable artifact.", |
| "weight": 16.6667, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml17-exec-seeds-runtime", |
| "requirements": "Execution repeats stochastic methods with multiple seeds per dataset, reports dispersion, and logs a wall-clock timing measure per condition. Honest small-seed runs with variance reported are preferable to a single run.", |
| "weight": 8.3333, |
| "sub_tasks": [], |
| "task_category": "Code Execution", |
| "finegrained_task_category": "Experimental Setup" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| }, |
| { |
| "id": "ml17-results", |
| "requirements": "Quantitative analysis addresses H1/H2/H3 directionally.", |
| "weight": 3, |
| "sub_tasks": [ |
| { |
| "id": "ml17-result-h1", |
| "requirements": "The submission compares NMF vs LSA on coherence for each evaluated dataset and conveys whether NMF tends to yield better coherence \u2014 judge directionally against H1.", |
| "weight": 20.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml17-result-h2", |
| "requirements": "The submission checks ranking agreement between coherence and ARI across methods per dataset and conveys whether coherence-ranking and ARI-ranking tend to diverge (H2).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| }, |
| { |
| "id": "ml17-result-h3", |
| "requirements": "The submission reports LDA vs LSA ARI deltas per dataset and conveys whether LDA tends to achieve meaningfully higher document-cluster ARI (H3).", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Evaluation, Metrics & Benchmarking" |
| }, |
| { |
| "id": "ml17-result-writeup", |
| "requirements": "The README or writeup describes methods and preprocessing, reports coherence/ARI/runtime results, conveys per-hypothesis outcomes (supported / refuted / inconclusive), and notes limitations (subset choice, coherence approximation validity, seed count, clustering-assignment assumptions). No strict word-count requirement.", |
| "weight": 10.0, |
| "sub_tasks": [], |
| "task_category": "Result Analysis", |
| "finegrained_task_category": "Logging, Analysis & Presentation" |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
| ], |
| "task_category": null, |
| "finegrained_task_category": null |
| } |
|
|
