Natural Language Processing Lecture
Acoustic digital signals are ubiquitous and have many applications in several disciplines, notably in:
-Digital Media, Social Media (music, voice signals),
-Biomedical Signal Analysis and Diagnosis,
-Scientific signal acquisition of any sort, e.g., Environment Sensing, Geophysical Prospecting.
Text can be considered as 1D signal, as it evolves over time (actually as an order time series of letters or words). Its analysis is extremely important in social media applications (e.g., Tweet analysis), in the analysis of any written and/or broadcasted text (e.g., news articles in newspapers) and in literary text analysis.

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.