tetun-tokenizer 1.2.3

Tetun tokenizer

Tetun Tokenizer

Tetun tokenizer is a Python package used to tokenize an input text into tokens. It offers several tokenization techniques as follows:

1. TetunStandardTokenizer: Segments the input text into individual tokens based on word boundaries, punctuation, and special characters.
2. TetunSentenceTokenizer: Splits sentences using ending delimiters such as periods (.), question marks (?), and exclamation marks (!). Titles represented by periods, such as Dr. and Ph.D., are preserved.
3. TetunBlankLineTokenizer: Segments the input text based on the presence of blank lines.
4. TetunSimpleTokenizer: Extracts only strings and numbers from the input text while discarding punctuation and special characters.
5. TetunWordTokenizer: Extracts word units from the input text, excluding numbers, punctuation, and special characters.

Installation

You can install the Tetun tokenizer via pip:

pip install tetun-tokenizer

Usage

To utilize the Tetun tokenizer, simply import the desired tokenizer feature/class from the tokenizer module within the Tetun tokenizer package. Here are some examples demonstrating its usage:

1. Utilizing TetunStandardTokenizer to tokenize the input text.

from tetuntokenizer.tokenizer import TetunStandardTokenizer

# Instantiate Tetun Standard Tokenizer class
tetun_tokenizer = TetunStandardTokenizer()

# Input text
text = "Ha'u mak ita-nia maluk di'ak. Ha'u iha $0.25 atu fó ba ita."

# Tokenize the input text
output = tetun_tokenizer.tokenize(text)

# Print the output
print(output)

Expected output:

["Ha'u", 'mak', 'ita-nia', 'maluk', "di'ak", '.', "Ha'u", 'iha', '$', '0.25', 'atu', 'fó', 'ba', 'ita', '.']

2. Using TetunSentenceTokenizer to tokenize the input text.

from tetuntokenizer.tokenizer import TetunSentenceTokenizer

tetun_tokenizer = TetunSentenceTokenizer()

text = "Ha'u ema-ida ne'ebé baibain de'it. Tebes ga? Ita-nia maluk Dr. ka Ph.D sira husi U.S.A mós dehan!"
output = tetun_tokenizer.tokenize(text)
print(output)

Expected output:

["Ha'u ema-ida ne'ebé baibain de'it.", 'Tebes ga?', 'Ita-nia maluk Dr. ka Ph.D sira husi U.S.A mós dehan!']

3. Utilizing TetunBlankLineTokenizer to tokenize the input text.

from tetuntokenizer.tokenizer import TetunBlankLineTokenizer

tetun_tokenizer = TetunBlankLineTokenizer()

text = """
        Ha'u mak ita-nia maluk di'ak.
        Ha'u iha $0.25 atu fó ba ita.
        """
output = tetun_tokenizer.tokenize(text)
print(output)

Expected output:

["\n            Ha'u mak ita-nia maluk di'ak.\n            Ha'u iha $0.25 atu fó ba ita.\n            "]

4. Using TetunSimpleTokenizer to tokenize a given text.

from tetuntokenizer.tokenizer import TetunSimpleTokenizer

tetun_tokenizer = TetunSimpleTokenizer()

text = "Ha'u mak ita-nia maluk di'ak. Ha'u iha $0.25 atu fó ba ita."
output = tetun_tokenizer.tokenize(text)
print(output)

Expected output:

["Ha'u", 'mak', 'ita-nia', 'maluk', "di'ak", "Ha'u", 'iha', '0.25', 'atu', 'fó', 'ba', 'ita']

5. Using TetunWordTokenizer to tokenize the input text.

from tetuntokenizer.tokenizer import TetunWordTokenizer

tetun_tokenizer = TetunWordTokenizer()

text = "Ha'u mak ita-nia maluk di'ak. Ha'u iha $0.25 atu fó ba ita."
output = tetun_tokenizer.tokenize(text)
print(output)

Expected output:

["Ha'u", 'mak', 'ita-nia', 'maluk', "di'ak", "Ha'u", 'iha', 'atu', 'fó', 'ba', 'ita']

Citation

If you use this repository or any of its contents for your research, academic work, or publication, we kindly request that you cite it as follows:

@inproceedings{de-jesus-nunes-2024-labadain-crawler,
    title = "Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus",
    author = "de Jesus, Gabriel  and
      Nunes, S{\'e}rgio Sobral",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.390",
    pages = "4368--4380"
}

Acknowledgement

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia under the PhD scholarship grant number SFRH/BD/151437/2021 (DOI 10.54499/SFRH/BD/151437/2021).

License

Additional Information

For the source code, visit the GitHub repository for this project.