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Datasets:
best2009

Tasks:
Token Classification
Languages: Thai
Multilinguality: monolingual
Size Categories: 100K<n<1M
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: original
Tags: word-tokenization
License: cc-by-nc-sa-3.0
Dataset card Files Community
2
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"article_00001.txt"
[ "āļ", "āļŽ", "āļŦ", "āļĄ", "āļē", "āļĒ", "āļ", "āļą", "āļš", "āļ", "āļē", "āļĢ", "āđ€", "āļš", "āļĩ", "āļĒ", "āļ”", "āļš", "āļą", "āļ‡", "āļ„", "āļ™", "āļˆ", "āļ™", "\n" ]
[ 1, 1, 3, 1, 10, 1, 1, 4, 1, 1, 10, 1, 11, 1, 10, 1, 1, 1, 4, 1, 1, 1, 1, 1, 4 ]
[ 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1 ]
"article_00001.txt"
[ "āļˆ", "āļē", "āļ", "āļ•", "āđ‰", "āļ™", "āļ‰", "āļš", "āļą", "āļš", "āđ€", "āļĢ", "āļ·", "āđˆ", "āļ­", "āļ‡", " ", "\"", "āļš", "āļ—", "āļ™", "āļģ", ":", " ", "āļ„", "āļ™", "āļˆ", "āļ™", "āļ ", "āļē", "āļĒ", "āđƒ", "āļ•", "āđ‰", "āļ„", "āļ§", "āļē", "āļĄ", "āļŠ", "āļą", "āļĄ", "āļž", "āļą", "āļ™", "āļ˜", "āđŒ", "āļ—", "āļē", "āļ‡", "āļ", "āļŽ", "āļŦ", "āļĄ", "āļē", "āļĒ", "\"", "\n" ]
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"article_00001.txt"
[ "āđ„", "āļž", "āļŠ", "āļī", "āļ", " ", "āļž", "āļē", "āļ“", "āļī", "āļŠ", "āļĒ", "āđŒ", "āļ", "āļļ", "āļĨ", "\n" ]
[ 11, 1, 1, 10, 1, 5, 1, 10, 1, 10, 1, 1, 7, 1, 10, 1, 4 ]
[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ]
"article_00001.txt"
[ "āļ­", "āļē", "āļˆ", "āļē", "āļĢ", "āļĒ", "āđŒ", "āļ›", "āļĢ", "āļ°", "āļˆ", "āļģ", " ", "āļŠ", "āļē", "āļ‚", "āļē", "āļ§", "āļī", "āļŠ", "āļē", "āļ™", "āļī", "āļ•", "āļī", "āļĻ", "āļē", "āļŠ", "āļ•", "āļĢ", "āđŒ", " ", "āļ ", "āļē", "āļ„", "āļ§", "āļī", "āļŠ", "āļē", "āļĢ", "āļą", "āļ", "āļĻ", "āļē", "āļŠ", "āļ•", "āļĢ", "āđŒ", " ", "āļ„", "āļ“", "āļ°", "āļŠ", "āļą", "āļ‡", "āļ„", "āļĄ", "āļĻ", "āļē", "āļŠ", "āļ•", "āļĢ", "āđŒ", " ", "āļĄ", "āļŦ", "āļē", "āļ§", "āļī", "āļ—", "āļĒ", "āļē", "āļĨ", "āļą", "āļĒ", "āđ€", "āļŠ", "āļĩ", "āļĒ", "āļ‡", "āđƒ", "āļŦ", "āļĄ", "āđˆ", "\n" ]
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"article_00001.txt"
[ "(", "āļš", "āļ—", "āļ„", "āļ§", "āļē", "āļĄ", "āļ™", "āļĩ", "āđ‰", "āļĒ", "āļē", "āļ§", "āļ›", "āļĢ", "āļ°", "āļĄ", "āļē", "āļ“", " ", "9", " ", "āļŦ", "āļ™", "āđ‰", "āļē", "āļ", "āļĢ", "āļ°", "āļ”", "āļē", "āļĐ", " ", "A", "4", ")", "\n" ]
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"article_00001.txt"
[ "āļ„", "āļ§", "āļē", "āļĄ", "āļ™", "āļģ", "\n" ]
[ 1, 1, 10, 1, 1, 10, 4 ]
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"article_00001.txt"
[ "āļ„", "āļ™", "āļˆ", "āļ™", "āļ ", "āļē", "āļĒ", "āđƒ", "āļ•", "āđ‰", "āļ„", "āļ§", "āļē", "āļĄ", "āļŠ", "āļą", "āļĄ", "āļž", "āļą", "āļ™", "āļ˜", "āđŒ", "āļ—", "āļē", "āļ‡", "āļ", "āļŽ", "āļŦ", "āļĄ", "āļē", "āļĒ", " ", "āļ‡", "āļē", "āļ™", "āļŠ", "āļī", "āđ‰", "āļ™", "āļ™", "āļĩ", "āđ‰", "āļĄ", "āļĩ", "āļ", "āļē", "āļ™", "āļ°", "āđ€", "āļ›", "āđ‡", "āļ™", "āđ€", "āļž", "āļĩ", "āļĒ", "āļ‡", "āļš", "āļ—", "āļ™", "āļģ", "āļ—", "āļē", "āļ‡", "āļ„", "āļ§", "āļē", "āļĄ", "āļ„", "āļī", "āļ”", " ", "āđ€", "āļž", "āļ·", "āđˆ", "āļ­", "āļ›", "āļđ", "āļ—", "āļē", "āļ‡", "āđ„", "āļ›", "āļŠ", "āļđ", "āđˆ", "āļ", "āļē", "āļĢ", "āļ—", "āļģ", "āļ„", "āļ§", "āļē", "āļĄ", "āđ€", "āļ‚", "āđ‰", "āļē", "āđƒ", "āļˆ", "āļ„", "āļ§", "āļē", "āļĄ", "āļŠ", "āļą", "āļĄ", "āļž", "āļą", "āļ™", "āļ˜", "āđŒ", "āļĢ", "āļ°", "āļŦ", "āļ§", "āđˆ", "āļē", "āļ‡", " ", "\"", "āļ„", "āļ™", "āļˆ", "āļ™", "\"", " ", "āļ", "āļą", "āļš", " ", "\"", "āļ›", "āļą", "āļ", "āļŦ", "āļē", "āļ„", "āļ§", "āļē", "āļĄ", "āđ„", "āļĄ", "āđˆ", "āđ€", "āļŠ", "āļĄ", "āļ­", "āļ ", "āļē", "āļ„", "āđ", "āļĨ", "āļ°", "āļ„", "āļ§", "āļē", "āļĄ", "āđ„", "āļĄ", "āđˆ", "āđ€", "āļ›", "āđ‡", "āļ™", "āļ˜", "āļĢ", "āļĢ", "āļĄ", "\"", " ", "āļ—", "āļĩ", "āđˆ", "āđ€", "āļ", "āļī", "āļ”", "āļˆ", "āļē", "āļ", "āļĢ", "āļ°", "āļš", "āļš", "āļ", "āļŽ", "āļŦ", "āļĄ", "āļē", "āļĒ", " ", "āđ", "āļĨ", "āļ°", "āđƒ", "āļ™", "āļ", "āļē", "āļ™", "āļ°", "āļ—", "āļĩ", "āđˆ", "āđ€", "āļ›", "āđ‡", "āļ™", "āļš", "āļ—", "āļ™", "āļģ", "āļ—", "āļē", "āļ‡", "āļ„", "āļ§", "āļē", "āļĄ", "āļ„", "āļī", "āļ”", "\n" ]
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"article_00001.txt"
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"article_00001.txt"
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Dataset Card for best2009

Dataset Summary

best2009 is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by NECTEC (148,995/2,252 lines of train/test). It was created for BEST 2010: Word Tokenization Competition. The test set answers are not provided publicly.

Supported Tasks and Leaderboards

word tokenization

Languages

Thai

Dataset Structure

Data Instances 

{'char': ['?', 'āļ ', 'āļđ', 'āļĄ', 'āļī', 'āļ›', 'āļą', 'āļ', 'āļ', 'āļē', 'āļŠ', 'āļē', 'āļ§', 'āļš', 'āđ‰', 'āļē', 'āļ™', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]}
{'char': ['āļ ', 'āļđ', 'āļĄ', 'āļī', 'āļ›', 'āļą', 'āļ', 'āļ', 'āļē', 'āļŠ', 'āļē', 'āļ§', 'āļš', 'āđ‰', 'āļē', 'āļ™', ' ', 'āļŦ', 'āļĄ', 'āļē', 'āļĒ', 'āļ–', 'āļķ', 'āļ‡', ' ', 'āļ„', 'āļ§', 'āļē', 'āļĄ', 'āļĢ', 'āļđ', 'āđ‰', 'āļ‚', 'āļ­', 'āļ‡', 'āļŠ', 'āļē', 'āļ§', 'āļš', 'āđ‰', 'āļē', 'āļ™', ' ', 'āļ‹', 'āļķ', 'āđˆ', 'āļ‡', 'āđ€', 'āļĢ', 'āļĩ', 'āļĒ', 'āļ™', 'āļĢ', 'āļđ', 'āđ‰', 'āļĄ', 'āļē', 'āļˆ', 'āļē', 'āļ', 'āļž', 'āđˆ', 'āļ­', 'āđ', 'āļĄ', 'āđˆ', ' ', 'āļ›', 'āļđ', 'āđˆ', 'āļĒ', 'āđˆ', 'āļē', 'āļ•', 'āļē', 'āļĒ', 'āļē', 'āļĒ', ' ', 'āļ', 'āļē', 'āļ•', 'āļī', 'āļž', 'āļĩ', 'āđˆ', 'āļ™', 'āđ‰', 'āļ­', 'āļ‡', ' ', 'āļŦ', 'āļĢ', 'āļ·', 'āļ­', 'āļœ', 'āļđ', 'āđ‰', 'āļĄ', 'āļĩ', 'āļ„', 'āļ§', 'āļē', 'āļĄ', 'āļĢ', 'āļđ', 'āđ‰', 'āđƒ', 'āļ™', 'āļŦ', 'āļĄ', 'āļđ', 'āđˆ', 'āļš', 'āđ‰', 'āļē', 'āļ™', 'āđƒ', 'āļ™', 'āļ—', 'āđ‰', 'āļ­', 'āļ‡', 'āļ–', 'āļī', 'āđˆ', 'āļ™', 'āļ•', 'āđˆ', 'āļē', 'āļ‡', 'āđ†', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]}

Data Fields

  • fname: file name; also marks if article is articles, news, encyclopedia or novels
  • char: characters
  • char_type: character types as adopted from by deepcut
  • is_beginning: is beginning of word

Data Splits

train test
# lines 148,995 2,252
avg words per line 39.05 NA
total words 5,818,521 NA
avg characters per line 140.39 202.79
total characters 20,918,132 456,684
# lines articles 16,990 NA
# lines encyclopedia 50,631 NA
# lines novels 50,140 NA
# lines news 31,234 NA

Dataset Creation

Curation Rationale

The dataset was created for BEST 2010: Word Tokenization Competition by NECTEC.

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

Respective authors of the articles, news, encyclopedia and novels

Annotations

Annotation process

Detailed annotation guidelines can be found in BEST_Guideline_Release1.pdf as part of the uncompressed files. Word tokenization standard used was InterBEST2009

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

All data are curated from public sources. No personal and sensitive information is expected to be included.

Considerations for Using the Data

Social Impact of Dataset

  • word tokenization dataset from articles, news, encyclopedia and novels

Discussion of Biases

  • texts are relatively formal ones from articles, news, encyclopedia and novels.
  • word tokenization standard used was InterBEST2009.

Other Known Limitations

  • some tags unrelated to word tokenization (<NE> and <AB>) are cleaned out.
  • no word boundary provdied for the test set

Additional Information

Dataset Curators

NECTEC

Licensing Information

CC-BY-NC-SA 3.0

Citation Information

Dataset:

@inproceedings{kosawat2009best,
  title={BEST 2009: Thai word segmentation software contest},
  author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
  booktitle={2009 Eighth International Symposium on Natural Language Processing},
  pages={83--88},
  year={2009},
  organization={IEEE}
}
@inproceedings{boriboon2009best,
  title={Best corpus development and analysis},
  author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
  booktitle={2009 International Conference on Asian Language Processing},
  pages={322--327},
  year={2009},
  organization={IEEE}
}

Character type features:

@inproceedings{haruechaiyasak2009tlex,
  title={TLex: Thai lexeme analyser based on the conditional random fields},
  author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot},
  booktitle={Proceedings of 8th International Symposium on Natural Language Processing},
  year={2009}
}

Contributions

Thanks to @cstorm125 for adding this dataset.

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Size of downloaded dataset files:
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Number of rows:
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Models trained or fine-tuned on best2009