Скачать или смотреть Project Name : Implement Multi Language Tokenizer using a Project

Project Name : Implement Multi Language Tokenizer using a Project

Overview
This project builds an advanced Multi-Language Tokenizer that automatically detects the input language and applies language-specific tokenization for English, Hindi, Arabic, and Chinese. It visualizes token statistics through frequency tables and bar charts, providing an intuitive and modular interface for multilingual text processing.

We have :-

A diverse set of multilingual input texts, including English, Hindi, Arabic, and Chinese, representing different language families and tokenization complexities.

A foundational understanding of Python, natural language processing (NLP), and libraries such as NLTK, SpaCy, jieba, CAMeL Tools, and IndicNLP.

Tools to perform automatic language detection, language-specific tokenization, and token frequency analysis using both tabular and visual outputs.

We will:

Automatically detect the language of input text using robust language identification techniques to ensure accurate downstream tokenization.

Apply language-specific tokenization strategies for English, Hindi, Arabic, and Chinese using NLP libraries like SpaCy, IndicNLP, CAMeL Tools, and jieba.

Visualize the extracted tokens through structured tables and frequency bar charts, enabling intuitive exploration of multilingual token patterns and their linguistic characteristics.

Goal:
The goal of this project is to develop an intelligent, language-aware tokenization system capable of automatically detecting the input language and applying accurate, language-specific tokenization techniques for English, Hindi, Arabic, and Chinese. This system aims to support multilingual text processing by generating interpretable token outputs along with visualizations that highlight token frequency and linguistic structure.

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