Datasets:
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
- found
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-Common-Crawl
- original
task_categories:
- other
task_ids:
- other-other-text-to-speech
- other-other-web-search
Dataset Card for Common Crawl Domain Names
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/google-research-datasets/common-crawl-domain-names
- Repository: https://github.com/google-research-datasets/common-crawl-domain-names
- Paper: https://arxiv.org/pdf/2011.03138
- Leaderboard:
- Point of Contact:
Dataset Summary
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries (e.g. "commoncrawl" to "common crawl").
Breaking domain names such as "openresearch" into component words "open" and "research" is important for applications such as Text-to-Speech synthesis and web search. Common Crawl is an open repository of web crawl data that can be accessed and analyzed by anyone. Specifically, we scraped the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so we had to manually annotate the data.
Supported Tasks and Leaderboards
- Text-to-Speech synthesis
- Web search
Languages
en: English
Dataset Structure
Data Instances
Each sample is an example of space separated segments of a domain name. The examples are stored in their original letter casing, but harder and more interesting examples can be generated by lowercasing the input first.
For example:
Open B S D
NASA
ASAP Workouts
Data Fields
example: astringfeature: space separated segments of a domain name.
Data Splits
| split | size | trivial | avg_input_length | avg_segments |
|---|---|---|---|---|
| train | 17572 | 13718 | 12.63 | 2.65 |
| eval | 1953 | 1536 | 12.77 | 2.67 |
| test | 2170 | 1714 | 12.63 | 2.66 |
Dataset Creation
Curation Rationale
The dataset was curated by scraping the plaintext (WET) extracts for domain names from URLs that contained diverse letter casing (e.g. "OpenBSD"). Although in the previous example, segmentation is trivial using letter casing, this was not always the case (e.g. "NASA"), so the curators of the dataset had to manually annotate the data.
Source Data
Initial Data Collection and Normalization
Corpus of domain names scraped from Common Crawl and manually annotated to add word boundaries
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
The annotators are the curators of this dataset
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
The curators of this dataset are Jae Hun Ro and mwurts4google, who are the contributors of the official Github repository for this dataset. Since the account handles of other curators are unknown currently, the authors of the paper linked to this dataset is mentioned here as curators, Hao Zhang, Jae Ro, and Richard Sproat.
Licensing Information
Citation Information
@inproceedings{zrs2020urlsegmentation,
title={Semi-supervised URL Segmentation with Recurrent Neural Networks Pre-trained on Knowledge Graph Entities},
author={Hao Zhang and Jae Ro and Richard William Sproat},
booktitle={The 28th International Conference on Computational Linguistics (COLING 2020)},
year={2020}
}
Contributions
Thanks to @Karthik-Bhaskar for adding this dataset.