Project
Amharic Text processing and Information Retrieval
Year
2023
Service
NLP, Tokenization
Inforamation Retrieval System
Inforamation Retrieval System
Project Overview
Developed an Amharic Information Retrieval System using Python to improve document search for Amharic text. The system processes documents through tokenization, stop-word removal, and stemming, then applies TF-IDF weighting and Cosine Similarity to rank documents based on query relevance. This project demonstrates practical experience with natural language processing, indexing, and search algorithms for low-resource languages.
Project Overview
Developed an Amharic Information Retrieval System using Python to improve document search for Amharic text. The system processes documents through tokenization, stop-word removal, and stemming, then applies TF-IDF weighting and Cosine Similarity to rank documents based on query relevance. This project demonstrates practical experience with natural language processing, indexing, and search algorithms for low-resource languages.
The Problem
Despite being the official working language of Ethiopia and spoken by millions, Amharic lacks well-developed tools for digital text processing and search. Most existing search and information retrieval systems are designed for English and other widely supported languages. As a result, retrieving relevant information from Amharic documents is difficult due to the absence of standardized resources such as stop-word lists, stemming tools, and structured indexing systems. This limits efficient search, document analysis, and information access for Amharic text.
The Problem
Despite being the official working language of Ethiopia and spoken by millions, Amharic lacks well-developed tools for digital text processing and search. Most existing search and information retrieval systems are designed for English and other widely supported languages. As a result, retrieving relevant information from Amharic documents is difficult due to the absence of standardized resources such as stop-word lists, stemming tools, and structured indexing systems. This limits efficient search, document analysis, and information access for Amharic text.
The Solution
I contributed to the development of an Amharic Information Retrieval System built with Python, designed to improve how Amharic documents are processed, indexed, and searched. The system implements a complete text-processing pipeline that extracts text from documents, performs tokenization and normalization, removes stop words, and applies stemming techniques. It then builds an inverted index to enable efficient document retrieval.
The Solution
I contributed to the development of an Amharic Information Retrieval System built with Python, designed to improve how Amharic documents are processed, indexed, and searched. The system implements a complete text-processing pipeline that extracts text from documents, performs tokenization and normalization, removes stop words, and applies stemming techniques. It then builds an inverted index to enable efficient document retrieval.
The Result
The system successfully demonstrates how traditional information retrieval techniques can be adapted for the Amharic language. By implementing preprocessing, indexing, and similarity-based ranking, the project improves the ability to search and retrieve relevant Amharic documents. It also highlights practical approaches to handling low-resource languages and showcases my experience working with natural language processing, data processing pipelines, and search algorithms in Python.
The Result
The system successfully demonstrates how traditional information retrieval techniques can be adapted for the Amharic language. By implementing preprocessing, indexing, and similarity-based ranking, the project improves the ability to search and retrieve relevant Amharic documents. It also highlights practical approaches to handling low-resource languages and showcases my experience working with natural language processing, data processing pipelines, and search algorithms in Python.
