Metadata-Version: 2.1
Name: vi-nlp-core
Version: 1.1.8
Summary: vi-core-nlp is a library that supports Vietnamese NER by pattern matching .
Home-page: https://github.com/demdecuong/vi-core-nlp/tree/master
Author: minhnp et al.
Author-email: nguyenphucminh2804@gmail.com
License: UNKNOWN
Keywords: vi_nlp_core
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# NER for Vietnamese Medical Appointment Chatbot

# Usage

**Extract person name**
```
text = "tôi cần đặt bác sĩ tạ biên cương"
print(extractor.extract_person_name(text)
```
```
{'entities': [{'start': 19, 'end': 32, 'entity': 'person_name', 'value': 'Tạ Biên Cương', 'confidence': 1.0, 'extractor': 'pattern'}]}
```

**Extract Date**   
the value is the timestamp value
```
text = "tôi sinh vào ngày 21-3-1997"
extractor.extract_date(text)
```
```
{'entities': [{'start': 0, 'end': 5, 'entity': 'time', 'value': 1628562600.0, 'confidence': 1.0, 'extractor': 'absolute_pattern'}]}
```
**Extract Time**
```
text = '14:50 ngày 7 tháng 6'
print(extractor.extract_time(text,return_value=True)) #return value only
```
```
{'entities': [{'start': 7, 'end': 9, 'entity': 'time', 'value': 1628560800.0, 'confidence': 1.0, 'extractor': 'absolute_pattern'}]}
```

**Map department/gender to keys**
```
text = 'rai'
res = extractor.map_gender_to_key(text)
print(res)
# {'key': 'GEN01', 'text': 'rai', 'value': 'trai'}
text = 'tiêu hóa'
res = extractor.map_dep_to_key(text)
print(res)
# {'key': 'SP008', 'text': 'tiêu hóa', 'value': 'tiêu hóa'}
```

**From symptoms to Department**
```
text = "dạo này tôi thấy trong người mệt mỏi, thần kinh căng thẳng do cách ly covid quá lâu"
res = extractor.extract_symptoms(text)
```
```
{'entities': [{'start': 29, 'end': 32, 'entity': 'symptom', 'value': 'mệt', 'confidence': 1.0, 'extractor': 'fuzzy_matching'}, {'start': 39, 'end': 48, 'entity': 'symptom', 'value': 'thần kinh', 'confidence': 1.0, 'extractor': 'fuzzy_matching'}, {'start': 49, 'end': 59, 'entity': 'symptom', 'value': 'căng thẳng', 'confidence': 1.0, 'extractor': 'fuzzy_matching'}]}
```
Extract Department Keys directly
```
res = extractor.extract_symptoms(text,get_dep_keys=True,top_k=3) 
```
```
[('SP012', 1.0), ('SP001', 0.0), ('SP002', 0.0)]
```
**Search department from list of symptoms**
```
text = ['ho', 'sổ mũi', 'đau họng', 'đau đầu', 'nghẹt mũi']
res = extractor.get_department_from_symptoms(text,top_k=3)
```
```
[('SP018', 1.0), ('SP006', 0.6), ('SP009', 0.4)]
```

