--- pretty_name: PoliInformatics 2014 — NLP Unshared Task on the 2008 Financial Crisis license: other license_name: research-use-as-distributed-by-organisers task_categories: - text-classification - text-generation - summarization - feature-extraction language: - en tags: - political-economy - financial-crisis - congressional-hearings - fomc - federal-reserve - financial-regulation - dodd-frank - tarp - fcic size_categories: - 10K. This release **streams** the upstream tarball (`unshared-task-poliinformatics-2014-v1.0.tgz`, 222 MB; mirrored at [alvations/stash](https://huggingface.co/datasets/alvations/stash/tree/main/poliinformatics-2014)) directly into parquet — no on-disk extraction — and exposes both the **cleaned text** (one config per subcorpus) and the **raw binary blobs** of every original file as a separate config. ## Configs | Config | Granularity | Source | Rows | Parquet | | ------------------------ | ----------- | ------ | ---: | ------: | | `congressional-bills` | one section per row | tab-delimited `.txt` from each bill | 7,184 | 9.3 MB | | `congressional-hearings` | one utterance per row | per-hearing `.csv` (Speaker, Speech) | 27,406 | 14.4 MB | | `fomc` | one utterance per row | per-meeting `.csv` (Discourse #, Speaker, Speech) | 6,287 | 3.8 MB | | `congressional-reports` | one report per row (HTML pages stripped + concatenated) | `raw-pages/*.html` | 2 | 1.2 MB | | `fcic` | one FCIC report per row (HTML pages stripped + concatenated) | `raw-pages/*.html` | 2 | 1.4 MB | | `raw` | one source file per row, binary blob | every PDF / HTML / CSV / TXT in the tarball | 13,565 | 368 MB | ## Usage ```python from datasets import load_dataset # utterance-level FOMC transcripts (Bernanke, Kohn, et al. on the financial crisis) fomc = load_dataset("alvations/poliinformatics-2014", "fomc", split="train") print(fomc[0]["speaker"], fomc[0]["text"][:120]) # CHAIRMAN BERNANKE. Good early morning. [Laughter] I would like to start by thanking the Committee for the very useful # TARP hearings — utterance-level hearings = load_dataset("alvations/poliinformatics-2014", "congressional-hearings", split="train") # Bills — section-level (Dodd-Frank, ARRA, Gramm-Leach-Bliley, 110thALL, 111thALL etc.) bills = load_dataset("alvations/poliinformatics-2014", "congressional-bills", split="train") print([d for d in set(bills["doc_id"])]) # Raw originals (PDF / HTML / CSV bytes) for users who want to redo the extraction raw = load_dataset("alvations/poliinformatics-2014", "raw", split="train") print(raw[0]["file_path"], raw[0]["file_type"], raw[0]["size_bytes"]) ``` ## Schemas ### `congressional-bills` | Column | Type | Description | | ------------- | ------- | ----------- | | `subcorpus` | string | always `"congressional-bills"` | | `doc_id` | string | bill name (e.g. `DoddFrank`, `ARRA`, `GrammLeachBliley`, `111thALL`) | | `congress` | int32 | Congress number (e.g. 111) | | `chamber` | string | `hr` / `s` / `hres` / `sres` / `hjres` / `sjres` / `hconres` / `sconres` | | `bill_no` | string | bill number within the chamber | | `version` | string | bill version code (e.g. `rfs`, `eas`, `enr`) | | `full_id` | string | concatenated bill identifier (e.g. `hr4173-111-eas`) | | `page_no` | int32 | section count for this bill version | | `date` | string | issued-on date (M/D/YY) | | `section_idx` | int32 | section index within the bill | | `char_count` | int32 | character length of the section text | | `text` | string | the section text | ### `congressional-hearings` / `fomc` (utterance-level) | Column | Type | Description | | --------------- | ------- | ----------- | | `subcorpus` | string | `"congressional-hearings"` or `"fomc"` | | `doc_id` | string | hearing / meeting identifier (e.g. `CHRG-110shrg50420`, `FOMC20071211meeting`) | | `topic` | string | only set for hearings (e.g. `TARP`); empty for FOMC | | `speaker` | string | speaker label as it appears in the transcript (e.g. `CHAIRMAN BERNANKE.`, `MR. KOHN.`) | | `utterance_idx` | int32 | row order within the source CSV | | `discourse_num` | int32 | discourse number when present (FOMC); −1 otherwise | | `text` | string | the utterance text | ### `congressional-reports` / `fcic` (document-level) Built by collecting all `raw-pages/*.html` (or `-raw/*.html`) files per report, BeautifulSoup-stripping the markup, and concatenating in page order. | Column | Type | Description | | ----------- | ------ | ----------- | | `subcorpus` | string | `"congressional-reports"` or `"fcic"` | | `doc_id` | string | report folder basename | | `title` | string | parent topic folder (e.g. `AnatomyofCollapse`, `StockMarketPlunge`, `FCICFinalReport`) | | `n_pages` | int32 | number of HTML pages concatenated | | `text` | string | cleaned plain-text body | ### `raw` | Column | Type | Description | | ------------ | ------- | ----------- | | `subcorpus` | string | the upstream top-level folder name | | `doc_id` | string | the second-level folder (or filename for bills) | | `file_path` | string | full path within the tarball | | `file_type` | string | extension (`pdf`, `html`, `htm`, `csv`, `txt`, `py`, …) | | `size_bytes` | int64 | file size | | `bytes` | binary | raw file contents | ## Build pipeline `build.py` streams the source tarball via `tarfile.ZipFile.open(mode="r:gz")` and: 1. For each `*.csv` in CongressionalHearings / FOMC — parse with `csv.DictReader` (after stripping NULs and lifting the field-size cap to handle very long speeches) and emit one row per utterance. 2. For each bill `.txt` — regex-scan for record anchors (`\d+\t\d+\t\d+\t\d+\t[a-z]+\t...`) since the source records are concatenated without line breaks and contain embedded quotes/newlines. Emit one row per section. 3. For each report / FCIC folder containing `*raw*/*.html` — collect the page bytes, BS-strip in page order, and emit one row per report. 4. For every regular file in the tarball — emit one row in the `raw` config with the bytes column. No PDFs are parsed; the HTML page extracts are the cleaner upstream form. ## Provenance - Upstream collected primarily by John Wilkerson (jwilker@uw.edu) and released by Noah Smith (nasmith@cs.cmu.edu). - Upstream README: - Contact: unsharedtask@gmail.com - Source mirror: [`alvations/stash · poliinformatics-2014/`](https://huggingface.co/datasets/alvations/stash/tree/main/poliinformatics-2014) ## Citation The official task description and discussion of the data live at the [Unshared Task 2014 site](https://sites.google.com/site/unsharedtask2014/data). The accompanying overview paper: ```bibtex @inproceedings{smith-etal-2014-poliinformatics, title = {Overview of the 2014 {NLP} Unshared Task in {P}oli{I}nformatics}, author = {Smith, Noah A. and Cardie, Claire and Washington, Anne L. and Wilkerson, John D.}, booktitle = {Proceedings of the {ACL} 2014 Workshop on Language Technologies and Computational Social Science}, pages = {5--7}, year = {2014}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/W14-2502/}, } ``` ## License The upstream release does not attach an explicit redistribution license, but the source materials are all US Government documents (Congressional bills, hearings, reports; Federal Reserve / FOMC meeting transcripts; FCIC reports) which are in the public domain in the United States. Reuse should still cite the PoliInformatics organisers and acknowledge the original collectors.