pip install parstdex
from parstdex import Parstdex
model = Parstdex()
sentence = """ماریا شنبه عصر راس ساعت ۱۷ و بیست و سه دقیقه به نادیا زنگ زد اما تا سه روز بعد در تاریخ ۱۸ شهریور سال ۱۳۷۸ ه.ش. خبری از نادیا نشد"""
model.extract_span(sentence)
output :
{"datetime": [[6, 47], [68, 78], [82, 111]], "date": [[6, 10], [68, 78], [82, 111]], "time": [[11, 47]]}
model.extract_marker(sentence)
{
"datetime":{
"[6, 47]":"شنبه عصر راس ساعت ۱۷ و بیست و سه دقیقه به",
"[68, 78]":"سه روز بعد",
"[82, 111]":"تاریخ ۱۸ شهریور سال ۱۳۷۸ ه.ش."
},
"date":{
"[6, 10]":"شنبه",
"[68, 78]":"سه روز بعد",
"[82, 111]":"تاریخ ۱۸ شهریور سال ۱۳۷۸ ه.ش."
},
"time":{
"[11, 47]":"عصر راس ساعت ۱۷ و بیست و سه دقیقه به"
}
}
model.extract_value(sentence)
output :
{
"date":{
"[6, 10]":"شنبه",
"[68, 78]":"3 روز بعد",
"[82, 111]":"1378/06/18"
},
"time":{
"[11, 47]":"17:23:00"
}
}
model.extract_ner(sentence)
output :
[('ماریا', 'O'),
('شنبه', 'B-DAT'),
('عصر', 'I-DAT'),
('راس', 'I-DAT'),
('ساعت', 'I-DAT'),
('۱۷', 'I-DAT'),
('و', 'I-DAT'),
('بیست', 'I-DAT'),
('و', 'I-DAT'),
('سه', 'I-DAT'),
('دقیقه', 'I-DAT'),
('به', 'I-DAT'),
('نادیا', 'O'),
('زنگ', 'O'),
('زد', 'O'),
('اما', 'O'),
('تا', 'O'),
('سه', 'B-DAT'),
('روز', 'I-DAT'),
('بعد', 'I-DAT'),
('در', 'O'),
('تاریخ', 'B-DAT'),
('۱۸', 'I-DAT'),
('شهریور', 'I-DAT'),
('سال', 'I-DAT'),
('۱۳۷۸', 'I-DAT'),
('ه', 'I-DAT'),
('.', 'I-DAT'),
('ش', 'I-DAT'),
('.', 'I-DAT'),
('خبری', 'O'),
('از', 'O'),
('نادیا', 'O'),
('نشد', 'O')]
Parstdex architecture is very flexible and scalable and therefore suggests an easy solution to adapt to new patterns which haven't been considered yet.
├── parstdex
│ └── utils
| | └── annotation
| | | └── ...
| | └── pattern
| | | └── ...
| | └── special_words
| | | └── words.txt
| | └── const.py
| | └── normalizer.py
| | └── pattern_to_regex.py
| | └── spans.py
| | └── word_to_value.py
| └── marker_extractor.py
| └── settings.py
└── Test
│ └── data.json
| └── test_parstdex.py
|
└── examples.py
└── performance_test.ipynb
└── requirement.txt
└── setup.py
Executable codes and performance test results are accessible on google colab.
The average time required to obtain temporal expressions is 6 ms
. This test was conducted using 264 sentences with an average length of 50 characters that covered all of the patterns.
Please feel free to provide us with any feedback or suggestions. You can find more information on how to contribute to Parstdex by reading the contribution document.
If you use any part of this library in your research, please cite it using the following BibTex entry.
@misc{parstdex,
author = {Kargaran, Amir Hossein and Mirzababaei, Sajad and Jahad, Hamid},
title = {Parstdex: Persian Time Date Extractor Python Library},
year = {2021},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/kargaranamir/parstdex}},
}