Update README.md
Browse files
README.md
CHANGED
|
@@ -9,9 +9,9 @@ pretty_name: ' RusLawOD '
|
|
| 9 |
size_categories:
|
| 10 |
- 100M<n<1B
|
| 11 |
---
|
| 12 |
-
# The Russian Legislative Corpus, 1991–
|
| 13 |
|
| 14 |
-
Russian primary and secondary legislation corpus covering laws of Russian Federation, decrees by the President of RF, regulations by the government published as of
|
| 15 |
|
| 16 |
For lemmatization, POS tagging, and dependency parsing we use the Ru-syntax tool developed by the Computational Linguistics team at the Higher School of Economics (Russia). This tool gathers the results from the morphological analyzer MyStem, part-of-speech tagging (Schmid, 2013) TreeTagger, and dependency grammar analyzer MaltParser. Finally, the result is stored in the CONLL-U format.
|
| 17 |
|
|
@@ -58,7 +58,7 @@ The corpus is stored in XML files each for one document. Every field appears if
|
|
| 58 |
```
|
| 59 |
|
| 60 |
## Metadata
|
| 61 |
-
|
| 62 |
|Node| Attribute| Comment| N| Unique, n| Missing, %|
|
| 63 |
|--- |--- |--- |---|---|---|
|
| 64 |
|body| textIPS | the text of a legal act | 302712 | – | 0.49 |
|
|
@@ -79,7 +79,7 @@ The corpus is stored in XML files each for one document. Every field appears if
|
|
| 79 |
| reference | classifierByIPS | official classifier | 243410 | - | 19.9 |
|
| 80 |
|
| 81 |
## Collection
|
| 82 |
-
We queried the online Legislation of Russia service (pravo.gov.ru) base called "IPS Zakonodatelstvo Rossii" maintained by the Special Communications Service for the texts legislation and their metadata. Our web scraping took place in the period spanning 2017—
|
| 83 |
|
| 84 |
## Usage
|
| 85 |
This dataset could be loaded in Python with [HF's Datasets library](https://huggingface.co/docs/datasets/index):
|
|
|
|
| 9 |
size_categories:
|
| 10 |
- 100M<n<1B
|
| 11 |
---
|
| 12 |
+
# The Russian Legislative Corpus, 1991–2026
|
| 13 |
|
| 14 |
+
Russian primary and secondary legislation corpus covering laws of Russian Federation, decrees by the President of RF, regulations by the government published as of July, 2026. The corpus collects all 308,056 texts (198,777,737 tokens) of non-secret federal regulations and acts, along with their metadata. The corpus has two versions: the original text with minimal preprocessing and a version prepared for linguistic analysis with morphosyntactic markup.
|
| 15 |
|
| 16 |
For lemmatization, POS tagging, and dependency parsing we use the Ru-syntax tool developed by the Computational Linguistics team at the Higher School of Economics (Russia). This tool gathers the results from the morphological analyzer MyStem, part-of-speech tagging (Schmid, 2013) TreeTagger, and dependency grammar analyzer MaltParser. Finally, the result is stored in the CONLL-U format.
|
| 17 |
|
|
|
|
| 58 |
```
|
| 59 |
|
| 60 |
## Metadata
|
| 61 |
+
Data description up to 2025:
|
| 62 |
|Node| Attribute| Comment| N| Unique, n| Missing, %|
|
| 63 |
|--- |--- |--- |---|---|---|
|
| 64 |
|body| textIPS | the text of a legal act | 302712 | – | 0.49 |
|
|
|
|
| 79 |
| reference | classifierByIPS | official classifier | 243410 | - | 19.9 |
|
| 80 |
|
| 81 |
## Collection
|
| 82 |
+
We queried the online Legislation of Russia service (pravo.gov.ru) base called "IPS Zakonodatelstvo Rossii" maintained by the Special Communications Service for the texts legislation and their metadata. Our web scraping took place in the period spanning 2017—2026 with the last data collection taking part in August, 2026.
|
| 83 |
|
| 84 |
## Usage
|
| 85 |
This dataset could be loaded in Python with [HF's Datasets library](https://huggingface.co/docs/datasets/index):
|