lexifuzz-ner 0.0.8

Python package for detecting entities in text based on a dictionary and fuzzy similarity

LexiFuzz NER: Named Entity Recognition Based on Dictionary and Fuzzy Matching

About

LexiFuzz NER is a Named Entity Recognition (NER) package designed to identify and extract named entities from unstructured text data. Leveraging a combination of dictionary-based and fuzzy matching techniques, LexiFuzz NER offers state-of-the-art accuracy in recognizing named entities in various domains, making it an invaluable tool for information extraction, natural language understanding, and text analytics.

Requirements

• Python 3.7 or Higher
• NLTK
• TheFuzz

Key Features

1. Dictionary-Based Recognition: LexiFuzz NER utilizes a comprehensive dictionary of named entities, encompassing a wide range of entities such as person names, organizations, locations, dates, and more. This dictionary is continuously updated to ensure high precision in entity recognition.

2. Fuzzy Matching: The package employs advanced fuzzy matching algorithms to identify named entities even in cases of typographical errors, misspellings, or variations in naming conventions. This ensures robustness in recognizing entities with varying textual representations.

3. Customization: LexiFuzz NER allows users to easily customize and expand the entity dictionary to suit specific domain or application requirements. This flexibility makes it adaptable to a wide array of use cases.

Usage

Manual Installation via Github

1. Clone Repository

   git clone https://github.com/hanifabd/lexifuzz-ner.git
   

2. Installation

   cd lexifuzz-ner && pip install .
   

Installation Using Pip

1. Installation

   pip install lexifuzz-ner
   

Inference

1. Usage

   from lexifuzz_ner.ner import find_entity
   
   dictionary = {
       'individual_product' : ['tahapan', 'xpresi', 'gold', 'berjangka'],
       'brand' : ["bca", "bank central asia"]
   }
   
   text = "i wanna ask about bca tahapn savings product"
   entities = find_entity(text, dictionary, 90)
   print(entities)
   

2. Result

   {
       'entities': [
           {
               'id': '55a20c6b-bd4a-43ee-8853-b961ac537ca8',
               'entity': 'bca',
               'category': 'brand',
               'score': 100,
               'index': {'start': 18, 'end': 20}},
           {
               'id': '08917da5-ed51-44bb-9be9-52f17df2640a',
               'entity': 'tahapn',
               'category': 'individual_product',
               'score': 92,
               'index': {'start': 22, 'end': 28}
           }
       ],
       'text': 'i wanna ask about bca tahapn savings product',
       'text_annotated': 'i wanna ask about [bca]{55a20c6b-bd4a-43ee-8853-b961ac537ca8} [tahapn]{08917da5-ed51-44bb-9be9-52f17df2640a} savings product'
   }