Dataset:
crawl_domain

Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: mit
Annotations Creators: expert-generated

Dataset Card for Common Crawl Domain Names

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: a string feature: 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

MIT License

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.

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Models trained or fine-tuned on crawl_domain

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