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Επεξεργασία Φυσικής Γλώσσας - Natural Language Processing (MSc CS & MSc ISDS)

INF210 - Ion Androutsopoulos

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Root directory slides_2024_25 The slides of 2024-25. Slides updated for 2025-26 may be gradually removed.

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	First Name	Size	Date
	nlp_slides_part08_dialogue_systems.pdf Introduction to spoken and written dialog systems. Systems that use rules, information retrieval, automata, grammars, frames, deep learning, pre-trained neural language models.	2.31 MB	12/27/24, 4:04 PM
	nlp_slides_part07_speech_recognition.pdf Introduction to automatic speech recognition (ASR). Encoding speech frames with pre-trained Transformers, wav2vec, HuBERT. ASR models: encoder/decoder models, encoder-only models. ASR evaluation measures. Optional older material: MFCC vectors, HMM models.	2.83 MB	12/6/24, 3:52 PM
	nlp_slides_part06_nlp_with_transformers.pdf Key-query-value attention, multi-head attention, Transformer encoders and decoders. Pre-trained Transformers and Large Language Models (LLMs), BERT, SMITH, BART, T5, GPT-3, InstructGPT, ChatGPT, and open-source alternatives, fine-tuning them, prompting them. Parameter efficient training, LoRA. Retrieval-augmented generation (RAG), LLMs with tools. Data augmentation for NLP. Adding vision to LLMs, LLaVA, InstructBLIP.	4.94 MB	12/30/24, 11:03 AM
	nlp_slides_part05_nlp_with_cnns.pdf Quick background on Convolutional Neural Networks (CNNs) in Computer Vision. Text processing with CNNs. Image to text generation with CNN encoders and RNN decoders.	2.31 MB	12/30/24, 11:52 AM
	nlp_slides_part04_nlp_with_rnns.pdf Recurrent neural networks (RNNs), GRUs/LSTMs. Applications in token classification (e.g., named entity recognition). RNN language models. RNNs with self-attention or global max-pooling, and applications in text classification. Bidirectional and stacked RNNs. Obtaining word embeddings from character-based RNNs. Hierarchical RNNs. Sequence-to-sequence RNN models with attention, applications in machine translation. Optional slides: Universal sentence encoders, LASER. Pre-training RNN language models, ELMo.	3.51 MB	12/30/24, 10:55 AM
	nlp_slides_part03_text_classification_with_mlps.pdf Perceptrons, training them with SGD, limitations. Multi-Layer Perceptrons (MLPs) and backpropagation. Dropout, batch and layer normalization. MLPs for text classification, regression, token classification (e.g., for POS tagging, named entity recognition). Pre-training word embeddings, Word2Vec. Advice for training large neural networks.	2.85 MB	12/30/24, 10:52 AM