About

TagLID labels each word in a Taglish (Tagalog-English mix) text by language. It gives either a simple tag (tgl or eng) or detailed frequency info with flags indicating how the word was identified. It is a rule-based, opinionated system that relies mostly on dictionary lookups, with additional logic for skipping numbers, names, and interjections, and for handling slang, abbreviations, contractions, stemming and lemmatizing inflected words, intrawords, and misspelling correction.

Installation

pip install taglid

Usage

TagLID can be used as a library via import taglid, or as a CLI application via python -m taglid.

Library

Textual data

Use the lid module for textual data.

Use lang_identify to identify each word in a text. It takes any string and returns a list of words with their corresponding English and Tagalog scores, flag, and correction.

from taglid.lid import lang_identify

labeled_text = lang_identify("hello, mundo")
print(labeled_text)

Output:

[{'Word': 'hello', 'eng': 1.0, 'tgl': 0.0, 'Flag': 'DICT', 'Correction': None}, {'Word': 'mundo', 'eng': 0.0, 'tgl': 1.0, 'Flag': 'DICT', 'Correction': None}]

Use tabulate to view the output in tabular format.

from tabulate import tabulate

print(tabulate(labeled_text, headers="keys"))

Output:

word      eng    tgl  flag    correction
------  -----  -----  ------  ------------
hello       1      0  DICT
mundo       0      1  DICT

Use simplify to reduce the output to only the words and their language. It takes the return value of lang_identify and returns a list of tuples containing the word and its language.

from taglid.lid import simplify

simplified_text = simplify(labeled_text)
print(simplified_text)

Output:

[('hello', 'eng'), ('mundo', 'tgl')]

Datasets

Use the lid_dataset module for datasets.

Use lang_identify_df to label each word in each cell of a pandas DataFrame. It takes a DataFrame of multiple rows and columns, where each cell contains textual data, and returns a labeled DataFrame where each token is a row labeled by its original row, original column, and token index.

import pandas as pd
from taglid.lid_dataset import lang_identify_df

data = [["hello po", "ano?"], ["mag-aask lang po", "what?"]]

df = pd.DataFrame(data)

labeled_df = lang_identify_df(df)
print(labeled_df)

Output:

     col  token_index      word  eng  tgl  flag correction
row
0      0            1     hello  1.0  0.0  DICT       None
0      0            2        po  0.0  1.0  DICT       None
0      1            1       ano  0.0  1.0  FREQ       None
1      0            1  mag-aask  0.5  0.5  INTW       None
1      0            2      lang  0.0  1.0  FREQ       None
1      0            3        po  0.0  1.0  DICT       None
1      1            1      what  1.0  0.0  DICT       None

CLI

Run TagLID from the terminal.

python -m taglid.lid

Then type a sentence when prompted.

text: hello, mundo

Output:

word      eng    tgl  flag    correction
------  -----  -----  ------  ------------
hello       1      0  DICT
mundo       0      1  DICT

Add --simplify to only show the words and their language.

python -m taglid.lid --simplify --text hello, mundo

Output:

-----  ---
hello  eng
mundo  tgl
-----  ---

Use lid_dataset with Excel files to directly label spreadsheets.

python -m taglid.lid_dataset in_path out_path

Accuracy

The accuracy hasn't been tested yet.

Development

This project uses uv for dependency management.

Clone the repo and sync dependencies (including dev and test groups):

git clone https://github.com/andrianllmm/taglid.git
cd taglid
uv sync --all-groups

Run the tests:

uv run pytest

Contributing

Contributions are welcome! See CONTRIBUTING.md for details.

License

Distributed under the MIT License.