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Ioannis Pitas (AUTH)
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This lecture overviews Natural Language Processing (NLP) that has many applications in text analytics, Linguistics, Machine translation and sentiment analysis. It covers the following topics in detail: Symbolic NLP, Statistical NLP, Neural NLP. NLP methods: Rules, Statistics, Neural networks. Word Representations: Fixed (sparse), One-hot encoding, Bag-of-words, TF-IDF Distributed (dense). Classic embeddings: Word2Vec, GloVe, FastText. Contextualized embeddings: CoVe, ELMo, OpenAI GPT, BERT. Common NLP Tasks: Automatic summarization, Book generation, Question answering, Machine translation.
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