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Evolution of Language Representation Techniques: A Journey from BoW to GPT-
- The evolution of language representation techniques started from simple methods like Bag-of-Words (BoW), which treated words as isolated tokens and ignored context. But, now advanced models like BERT and GPT enable machines to understand and generate coherent text.
- Language representation is the conversion of language into a format that machines can comprehend, analyze, interpret, and respond.
- Vectorization techniques are essential in this process that involves transforming text data into numerical vectors to perform mathematical operations, detect patterns and predict outcomes.
- Different types of language representation were developed, building upon limitations of its predecessors, such as Bag-of-Words, TF-IDF, Word Embeddings, BERT, and GPT models.
- Bag-of-Words or BoW was easy to implement but ignored word order and meaning, thus not adequate for understanding semantic relationships between words.
- TF-IDF was better than BoW as it highlighted important words in a document, but lacked in capturing word order and context to understand meaning.
- Word2Vec, GloVe, and similar models revolutionized NLP by capturing semantic relationships between words but did not understand context-dependent meanings.
- BERT and GPT models were bidirectional and self-supervised, which facilitated the deep contextual understanding of word meaning in sentences and coherent text generation for chatbots, content creation, and storytelling.
- These language representation models helped researchers generate efficient NLP applications like semantic similarity, sentiment analysis, recommendation systems, and machine translation.
- The understanding of the distinctions between these models can help choose the right tool for different NLP applications, creating more sophisticated language understanding and generation technologies.
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