bengalianalyzer 0.0.104

A package for analyzing entities present in Bengali sentence

Bengali (Bangla) Analyzer

This package provides an analyzer for Bengali (Bangla) language. We have gone through a dictionary entry based approach with grammatical sanitizing for this project. Here in our implementation we have 5 different type of entities:

• Prefix: Prefix or উপসর্গ is a substring in a word that generally does not hold a meaning of its own but when added to a word that has its own meaning, gets a new definition on it.

• Suffix: Suffix or অনুসর্গ is a trailing substring in a word that generally does not hold a meaning of its own but when added to a word that has its own meaning, gets a new definition on it.

• Verb: Any word or group of words that describe the action, state or occurrence of an event in a Bengali sentence. For example - খাওয়া, চলে যাওয়া etc. etc .

• Non-verb: Any other remaining parts of speech that are not recognized as a verb in a Bengali sentence. For example - আমি, খুব, তারা, বাংলা, বয়স, etc. etc.

• Special entity: As the name suggests, a special entity can be a special date (for example, ২১ শে ফেব্রুয়ারী which is the International Mother Language Day), a person (for example - ড. মুহাম্মদ জাফর ইকবাল a famous author of science fictions and well-known professor), institute (for example - জাবি which is the abbreviation of Jahangirnagar University) or any other multi-word single entity.

• Composite word: Our structural definition of composite Bengali word is - Prefix (optional) + (One or) Multiple stand-alone Bengali words + Suffix (optional)

Our package analyzes the given text and returns the word configurations of the text according to the definitions we have chosen to give to the entities which could be present in a bengali sentence.

Installation

The package can be installed in any fashion. It is highly recommended to install Conda and then run the following command to install the package:

pip install bengalianlyzer

Local Environment

This is the environment in which the package was developed:

 ██████████████████  ████████     Python: 3.9.0
 ██████████████████  ████████     OS: Manjaro 21.2.3 Qonos
 ██████████████████  ████████     Kernel: x86_64 Linux 5.15.21-1-MANJARO
 ██████████████████  ████████     Conda: 4.10.3
 ████████            ████████     CPU: 11th Gen Intel Core i7-11370H @ 8x 4.8GHz 
 ████████  ████████  ████████     GPU: NVIDIA GeForce RTX 3060 Laptop GPU
 ████████  ████████  ████████     RAM: 15694MiB
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 ████████  ████████  ████████     
 ████████  ████████  ████████     
 ████████  ████████  ████████     
 ████████  ████████  ████████     
 ████████  ████████  ████████     
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Usage

Import the module first.

from bengalianalyzer import BengaliAnalyzer 

And then pass the text for analysis.

bl = BengaliAnalyzer()
tokens = bl.analyze_sentence(text)

Response

The response will return tokens (data type : dictionary) which has each token as its key. The following dimension will be present for each token:

tokens[token] = {
            "Global_Index": [int or (int, int)],
            "Punctuation_Flag": bool,
            "Numeric":
                {
                    "Digit": int,
                    "Literal": str,
                    "Weight": str,
                    "Suffix": [str]
                },
            "Verb":
                {
                    "Parent_Verb": str,
                    "Tense": str,
                    "Emphasis": [str],
                    "Form": str,
                    "Person": str,
                    "Related_Indices": [[int or (int,int)]]
                },
            "Non_Verb": str,
            "Composite_Word":
                {
                    "Suffix": str,
                    "Prefix": str,
                    "Stand_Alone_Words": {str},
                },
            "Special_Entity":
                {
                    "Definition": str,
                    "Related_Indices": [[int or (int,int)]]
                }
        }

Team

This tool is developed by people with diverse affiliations. The following are the people behind this effort.

Name	Email	Affiliation
Shahriar Elahi Dhruvo	[e-mail]	Shahjalal University of Science & Technology, Sylhet
Md. Rakibul Hasan Ranak	[e-mail]	Shahjalal University of Science & Technology, Sylhet
Mahfuzur Rahman Emon	[e-mail]	Shahjalal University of Science & Technology, Sylhet
Fazle Rabbi Rakib	[e-mail]	Shahjalal University of Science & Technology, Sylhet
Souhardya Saha Dip	[e-mail]	Shahjalal University of Science & Technology, Sylhet
Asif Shahriyar Shushmit	[e-mail]	Bengali.ai
A. A. Noman Ansary	[e-mail]	Govt. Laboratory High School, Rajshahi

Special thanks to Md Nazmuddoha Ansary for implementing an open source general purpose indic grapheme parser, which is a required dependency in this tool.

In collaboration with: Bengali.ai, SUST, RGLHS, Jahangirnagar University