amharicNLP 1.1.0

amharicNLP is a Python package for Amharic Natural Language Processing (NLP) and text preprocessing.

🇪🇹 Amharic NLP Toolkit

Amharic NLP Toolkit is a lightweight, powerful, and easy-to-use Natural Language Processing (NLP) toolkit designed specifically for the Amharic language. It provides complete tools for Amharic text preprocessing, cleaning, tokenization, normalization, stopword removal, stemming, lemmatization, and sentiment analysis.

Perfect for machine learning, deep learning, LLMs, AI projects, and any Ethiopian language application.

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🌍 Why Amharic Needs Its Own NLP Toolkit

Amharic — Ethiopia’s official language — is morphologically rich and syntactically complex.

A single Amharic word can contain:

✔️ Subject ✔️ Tense ✔️ Negation ✔️ Verb root ✔️ Suffix

Example: “አልሄደም” = negation + verb root + suffix.

Tools built for English (NLTK, SpaCy) cannot correctly handle:

• Fidel script
• Complex morphology
• Combined affixes
• Amharic punctuation
• Unicode inconsistencies

amharicNLP solves this challenge with a full, language-specific preprocessing pipeline.

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⚙️ What Is amharicNLP?

amharicNLP is a modular Python package built for end-to-end Amharic text preprocessing.

🧩 It includes six core components:

1. Cleaner – Removes HTML, emojis, numbers & noise
2. Normalizer – Fixes inconsistencies in characters & punctuation
3. Tokenizer – Splits text into meaningful tokens
4. Stopword Processor – Removes common filler words
5. Lemmatizer – Converts words to their base dictionary form
6. Stemmer – Reduces words to their root for ML tasks

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📦 Installation

Option 1: Install from PyPI (Recommended)

pip install amharicNLP

Option 2: Install Latest Development Version

git clone https://github.com/yonasab12/amharicNLP.git
cd amharicNLP
pip install .

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🧪 Full Demo: End-to-End Amharic Text Preprocessing

from amharicNLP.resources.cleaner import AmharicCleaner
from amharicNLP.resources.normalizer import AmharicNormalizer
from amharicNLP.resources.lemmatizer import AmharicLemmatizer
from amharicNLP.resources.stemmer import AmharicStemmer
from amharicNLP.resources.stopwrod import AmharicStopwordProcessor
from amharicNLP.resources.tokenizer import AmharicWordTokenizer

sample_text = "በአገራችን ኢትዮጵያ <h1/> ላ ያሉ ተማሪዎች በትምህርት ላይ ትኩረት ማድረግ አለባቸው። 123 ቁጥር! በላይ ዘለቀ ጀግና የኢትዮጵያ አርበኛ ነበር።"

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🧹 Step 1: Cleaning

cleaner = AmharicCleaner()
cleaned_html = cleaner.remove_html(sample_text)
cleaned_text = cleaner.remove_noise(cleaned_html)
print(cleaned_text)

Output

በአገራችን ኢትዮጵያ ላ ያሉ ተማሪዎች በትምህርት ላይ ትኩረት ማድረግ አለባቸው። ቁጥር በላይ ዘለቀ ጀግና የኢትዮጵያ አርበኛ ነበር።

✔️ HTML removed ✔️ Numbers & non-Amharic characters cleaned

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🔤 Step 2: Normalization

normalizer = AmharicNormalizer()
text1 = normalizer.normalize_amharic_chars(cleaned_text)
text2 = normalizer.normalize_punctuation_spacing(text1)
text3 = normalizer.expand_abbreviations(text2)
print(text3)

✔️ Standardized characters ✔️ Clean punctuation spacing

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🪶 Step 3: Stopword Removal

stopword_processor = AmharicStopwordProcessor()
filtered_text = stopword_processor.remove(text3)
print(filtered_text)

✔️ Removes high-frequency filler words

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📖 Step 4: Lemmatization

lemmatizer = AmharicLemmatizer()
lemmatized_text = lemmatizer.lemmatize(filtered_text)
print(lemmatized_text)

✔️ Converts words to canonical dictionary forms

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🌱 Step 5: Stemming

stemmer = AmharicStemmer()
stemmed = [stemmer.stemaize(word) for word in filtered_text]
print(stemmed)

✔️ Ideal for ML pipelines (text clustering, topic modeling)

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🧠 Why This Matters

amharicNLP significantly improves NLP performance by:

• Cleaning & normalizing messy text
• Reducing vocabulary sparsity
• Preparing text for downstream tasks like: ✔ Sentiment analysis ✔ Text classification ✔ POS tagging ✔ Named Entity Recognition (NER) ✔ Language modeling

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🧭 Module Summary

Step	Module	Purpose
1	AmharicCleaner	Removes noise, HTML, punctuation errors
2	AmharicNormalizer	Standardizes characters & spacing
3	AmharicWordTokenizer	Splits text into tokens
4	AmharicStopwordProcessor	Removes common stopwords
5	AmharicLemmatizer	Finds base word form
6	AmharicStemmer	Extracts root word

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🚀 Final Thoughts

amharicNLP bridges the gap between AI and one of Africa’s most important Semitic languages. With only a few lines of code, you can prepare Amharic data for machine learning, deep learning, and linguistic analysis.

“By teaching computers to understand Amharic, we make technology speak our language.” 🇪🇹💻

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✍️ Author

👤 Yonas Abebe

Exploring Amharic NLP, machine learning, and AI tools for Ethiopian languages. GitHub: yonasab12