v0.2.0: UFSAC training corpora (SemCor, WNGT, MASC, Senseval lex-sample)

README.md

@@ -10,10 +10,9 @@ tags:
   - wsd
   - wordnet
   - oewn
+  - semcor
   - semeval
   - senseval
-size_categories:
-  - 1K<n<10K
 configs:
   - config_name: en-senseval2-aw
     data_files:
@@ -35,6 +34,34 @@ configs:
     data_files:
       - split: test
         path: data/en-semeval2015-aw/test.parquet
+  - config_name: en-semcor
+    data_files:
+      - split: train
+        path: data/en-semcor/train.parquet
+  - config_name: en-wngt
+    data_files:
+      - split: train
+        path: data/en-wngt/train.parquet
+  - config_name: en-masc
+    data_files:
+      - split: train
+        path: data/en-masc/train.parquet
+  - config_name: en-senseval2_ls
+    data_files:
+      - split: train
+        path: data/en-senseval2_ls/train.parquet
+      - split: test
+        path: data/en-senseval2_ls/test.parquet
+  - config_name: en-senseval3_ls
+    data_files:
+      - split: train
+        path: data/en-senseval3_ls/train.parquet
+      - split: test
+        path: data/en-senseval3_ls/test.parquet
+  - config_name: en-semeval2007_t17_ls
+    data_files:
+      - split: test
+        path: data/en-semeval2007_t17_ls/test.parquet
 ---
 
 # pywsd-datasets
@@ -44,16 +71,31 @@ Unified Word Sense Disambiguation benchmark datasets, normalized to **modern
 
 Companion to [pywsd](https://pypi.org/project/pywsd/) ≥ 1.3.0.
 
-## What's in v0.1 (English-only, expanding)
+## What's shipped (v0.2)
+
+**English, test-only Raganato all-words benchmark:**
+
+| Config                | Instances | OEWN 2024 coverage |
+|-----------------------|-----------|--------------------|
+| `en-senseval2-aw`     | 2,282     | 99.43 %            |
+| `en-senseval3-aw`     | 1,850     | 99.51 %            |
+| `en-semeval2007-aw`   | 455       | 99.78 %            |
+| `en-semeval2013-aw`   | 1,644     | 100.00 %           |
+| `en-semeval2015-aw`   | 1,022     | 99.32 %            |
+
+**English, training corpora (via UFSAC v2.1):**
 
-| Config                | Source                 | Instances | OEWN 2024 coverage |
-|-----------------------|------------------------|-----------|--------------------|
-| `en-senseval2-aw`     | Raganato et al. 2017   | 2,282     | 99.43 %            |
-| `en-senseval3-aw`     | Raganato et al. 2017   | 1,850     | 99.51 %            |
-| `en-semeval2007-aw`   | Raganato et al. 2017   | 455       | 99.78 %            |
-| `en-semeval2013-aw`   | Raganato et al. 2017   | 1,644     | 100.00 %           |
-| `en-semeval2015-aw`   | Raganato et al. 2017   | 1,022     | 99.32 %            |
-| **Total**             |                        | **7,253** | **99.59 %**        |
+| Config                    | Split | OEWN 2024 coverage |
+|---------------------------|-------|--------------------|
+| `en-semcor`               | train | see coverage_report |
+| `en-wngt`                 | train | see coverage_report |
+| `en-masc`                 | train | see coverage_report |
+| `en-senseval2_ls`         | train + test | lexical-sample |
+| `en-senseval3_ls`         | train + test | lexical-sample |
+| `en-semeval2007_t17_ls`   | test  | lexical-sample |
+
+Run `python -m pywsd_datasets.scripts.coverage_report` locally to get
+up-to-date OEWN resolution rates after rebuilding.
 
 ## Install
 
@@ -65,8 +107,14 @@ pip install pywsd-datasets
 
 ```python
 from datasets import load_dataset
+
+# Raganato all-words evaluation set
 ds = load_dataset("alvations/pywsd-datasets", "en-senseval2-aw")
-ds["test"][0]
+
+# SemCor training data
+ds = load_dataset("alvations/pywsd-datasets", "en-semcor")
+
+ds["test"][0] if "test" in ds else ds["train"][0]
 # {'instance_id': 'd000.s000.t000', 'dataset': 'senseval2_aw',
 #  'split': 'test', 'lang': 'en',
 #  'tokens': ['The', 'art', 'of', 'change-ringing', ...],
@@ -80,8 +128,13 @@ ds["test"][0]
 ## Use via the loader package
 
 ```python
-from pywsd_datasets.loaders.raganato import iter_instances
-for inst in iter_instances("senseval2"):
+from pywsd_datasets.loaders.raganato import iter_instances as iter_raganato
+from pywsd_datasets.loaders.ufsac import iter_instances as iter_ufsac
+
+for inst in iter_raganato("senseval2"):
+    print(inst.target_lemma, inst.sense_ids_wordnet)
+
+for inst in iter_ufsac("semcor", "/path/to/ufsac-public-2.1"):
     print(inst.target_lemma, inst.sense_ids_wordnet)
 ```
 
@@ -89,8 +142,29 @@ for inst in iter_instances("senseval2"):
 
 ```bash
 pip install pywsd-datasets[dev]
-python -m pywsd_datasets.scripts.build_all              # parquet in data/
-python -m pywsd_datasets.scripts.coverage_report        # OEWN 2024 resolution %
+
+# Raganato only (always works, ~1 MB fetch from our GH release mirror)
+python -m pywsd_datasets.scripts.build_all
+
+# With UFSAC corpora — download ufsac-public-2.1 separately (see below)
+python -m pywsd_datasets.scripts.build_all \
+    --ufsac-root ~/.cache/pywsd-datasets/ufsac/ufsac-public-2.1
+
+# Coverage report across every built parquet:
+python -m pywsd_datasets.scripts.coverage_report
+```
+
+### UFSAC download
+
+UFSAC v2.1 is distributed as a single Google Drive tarball
+(`ufsac-public-2.1.tar.xz`, ~196 MB). Fetch with `gdown`:
+
+```bash
+pip install gdown
+mkdir -p ~/.cache/pywsd-datasets/ufsac
+gdown 'https://drive.google.com/uc?id=1kwBMIDBTf6heRno9bdLvF-DahSLHIZyV' \
+    -O ~/.cache/pywsd-datasets/ufsac/ufsac-public-2.1.tar.xz
+cd ~/.cache/pywsd-datasets/ufsac && tar -xf ufsac-public-2.1.tar.xz
 ```
 
 ## Schema
@@ -109,15 +183,29 @@ doc_id, sent_id
 `sense_ids_wordnet` is list-valued to handle multi-gold instances and any
 PWN-3.0 key that splits into multiple OEWN 2024 synsets.
 
-## Roadmap
+## Multilingual / XL-WSD / BabelNet — deferred
 
-* **v0.2** — UFSAC corpora (SemCor, WNGT, MASC, OMSTI, Senseval lexical-sample).
-* **v0.3** — WiC (CC-BY-NC), CoarseWSD-20.
-* **v0.4** — XL-WSD multilingual via BabelNet → WordNet → OMW. Initial
-  languages: it, de, fr, es, ja (then the full XL-WSD 18 over time).
+`loaders/xl_wsd.py` exists as a stub and raises `NotImplementedError`.
+`mappers/babelnet_to_wn.py` is similarly unused. **Why:**
+
+* XL-WSD uses BabelNet synset IDs as gold labels; resolving them to
+  modern `wn` lexicon IDs requires the BabelNet → PWN 3.0 bridge file,
+  which is distributed **only with a BabelNet academic license**.
+* XL-WSD itself is CC-BY-NC 4.0 — we don't redistribute the data.
+
+Reviving this path requires (a) a BabelNet license, (b) loading
+`bn_to_wn.txt` via `babelnet_to_wn.load_bn_to_pwn3_map()`, (c) selecting
+per-language OMW lexicons via `mappers.omw_lookup.lexicon_for(lang)`,
+then (d) chaining through `pwn3_to_oewn.pwn3_sensekey_to_wn(key, lexicon=...)`.
+All four pieces are in place — wiring them is blocked on the BabelNet
+mapping file. See the module docstrings for details.
+
+## Roadmap
 
-Loader stubs already live under `src/pywsd_datasets/loaders/` for ufsac
-and xl_wsd.
+* **v0.2** (this release): Raganato all-words evaluation + UFSAC training
+  corpora (SemCor, WNGT, MASC, Senseval lexical-sample).
+* **v0.3** (planned): WiC (CC-BY-NC — loader-only), CoarseWSD-20.
+* **Deferred:** XL-WSD multilingual (needs BabelNet academic license).
 
 ## Citation
 
@@ -125,13 +213,17 @@ If you use these datasets please cite the original sources:
 
 * Raganato, Camacho-Collados, Navigli (2017). *Word Sense Disambiguation:
   A Unified Evaluation Framework and Empirical Comparison.* EACL.
-* Plus the paper for whichever specific evaluation set you use
-  (Senseval-2 / 3, SemEval-2007 T17, SemEval-2013 T12, SemEval-2015 T13).
+* Vial, Lecouteux, Schwab (2018). *UFSAC: Unification of Sense Annotated
+  Corpora and Tools.* LREC.
+* Plus the specific evaluation or training set paper (Senseval-2 / 3,
+  SemEval-2007 T17, SemEval-2013 T12, SemEval-2015 T13, SemCor,
+  WNGT/Princeton Gloss Corpus, MASC).
 
 ## License
 
 MIT for the code. Each dataset keeps its original license — see the source
-papers. Raganato bundle and SemEval shared-task data are research-unrestricted.
+papers. Raganato bundle and SemEval shared-task data are
+research-unrestricted; UFSAC is MIT.
 
 ## Sense-ID mapping details
 
@@ -146,8 +238,8 @@ so the coverage report can flag them. Background:
 
 ## Known issues
 
-* `lcl.uniroma1.it` serves a mismatched TLS cert; the loader fetches the
-  Raganato bundle over HTTP. When this repo matures we'll mirror the zip
-  to the HF Hub release assets.
-* UFSAC v2.1 distribution is a Google Drive zip last updated 2018; same
-  mirror plan applies once licensing is confirmed.
+* The upstream Raganato zip at `http://lcl.uniroma1.it/wsdeval/` serves a
+  mismatched TLS cert; our loader prefers the mirror on this repo's
+  GitHub release assets and falls back to the original URL over HTTP.
+* UFSAC v2.1 is distributed as a Google Drive tarball; the loader assumes
+  you have it unpacked locally. A future release may mirror it.