Bushra-KB/Amharic-NLP-Tools-in-JAVA

This repository contains implementations of various Natural Language Processing (NLP) tasks and tools specifically for the Amharic language using Java. The goal is to provide a comprehensive set of tools to facilitate NLP research and development for Amharic. # Amharic-NLP-Tools-in-JAVA ## Amharic NLP Tools in Java This repository contains implementations of various Natural Language Processing (NLP) tasks and tools specifically for the Amharic language using Java. The goal is to provide a comprehensive set of tools to facilitate NLP research and development for Amharic. ## Features - **Tokenization**: Splitting text into words, sentences, or other meaningful units. - **Sentence Segmentation**: Dividing text into individual sentences. - **Sentence Boundary Detection**: Identifying the boundaries of sentences within a text. - **Normalization**: - Character normalization - Abbreviation substitution - Strange character, word, and symbol removal - Removal of emojis - Removal of emoticons - Removal of punctuations - Conversion of emoticons to words - Conversion of emojis to words - **StopWord Removal**: Removing common words that do not carry significant meaning. - **Noun Phrase Chunking**: Identifying and grouping noun phrases. - **Lemmatization**: Reducing words to their base or root form. - **Stemming**: Reducing words to their root form by removing suffixes. - **Named Entity Recognition (NER)**: Identifying and classifying named entities in text. - **Part-of-Speech (POS) Tagging**: Assigning parts of speech to each word in a sentence. - **Word-Sense Disambiguation**: Determining the correct meaning of a word based on context. - **Co-reference Resolution**: Identifying when different expressions refer to the same entity. - **Entity Linking**: Connecting entities mentioned in the text to their corresponding entries in a knowledge base. - **Terminology Extraction**: Extracting domain-specific terms from text. - **Discourse Parsing**: Analyzing the structure of discourse in text. - **Sentiment Analysis**: Determining the sentiment expressed in a piece of text. - **Text Classification**: Categorizing text into predefined categories. - **Language Modeling**: Building models to predict the next word in a sequence. - **Machine …