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This repository contains the code and PLODv2 dataset to train character-level language models (CLM) for abbreviation and long-form detection released with our LREC-COLING 2024 publication
In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis