Скачать или смотреть Project 1: English-Text to sign language conversion with the use of NLP- Fundamentals.

Building a text to sign language translator is crucial for fostering inclusivity and accessibility in communication for the deaf and hard of hearing community. By leveraging advanced natural language processing (NLP) techniques such as tokenization, lemmatization, POS tagging, named entity recognition (NER), vectorization, and word to vector models, we can bridge this gap.

Tokenization breaks down text into smaller units, ensuring accurate processing of linguistic elements. Lemmatization standardizes words to their base forms, facilitating consistent interpretation. POS tagging assigns grammatical labels to words, enabling accurate structural analysis. NER identifies and categorizes named entities, enhancing contextual understanding. Vectorization transforms text into numerical representations, enabling computational analysis. Word to vector models capture semantic relationships between words, enhancing language comprehension.

This project aims to scratch the potential of NLP fundamentals to develop Rule rule based translators which can provide seamless and accessible means of communication.