Representation Learning for NLP

Apr 30, 2017Download as pptx, pdf2 likes1,152 views

This document outlines a 6-hour course on representation learning for natural language processing. The course is divided into 4 modules that will provide both theoretical understanding and code implementations. Module 1 introduces text representation and different encoding methods. Module 2 focuses on word vectors and models like CBOW, Skip-gram, GloVe, and techniques like negative sampling and t-SNE visualization. Module 3 covers sentence and document representations with models like PV-DM, PV-DBOW, and Skip-Thoughts. Finally, Module 4 introduces character-level representations using RNNs, LSTMs, and character embeddings. The course aims to provide both conceptual understanding and hands-on coding experience of various NLP representation learning techniques.

Representation Learning for
NLP: Deep Dive
Anuj Gupta, Satyam Saxena

• Duration : 6 hrs
• Level : Intermediate to Advanced
• Objective: For each of the topics, we will dig into the concepts,
maths to build a theoretical understanding; followed by code
(jupyter notebooks) to understand the implementation details.

Module 1 (30 mins)
• Introduction to Text Representation (5 mins)
• Old ways of representing text (20 mins)
• Bag-Of-Words
• TF–IDF
• Co-occurrence matrix + SVD
• Pros and Cons
• Introduction to Embedding spaces (5 mins)
Outline/Time Map - 4 Modules

Module 2 (160 mins)
• Word-Vectors
• Introduction + Bigram model (25 mins)
• CBOW model (25 mins)
• SKIP-GRAM model (25 mins)
[Efficient estimation of word representations in vector space. Mikolov, et. al.
ICLR Workshop, 2013]
• Speed-Up (20 mins)
• Negative Sampling
• Hierarchical Softmax
[Distributed representations of words and phrases and their compositionality.
Mikolov, et. al. ANIPS, 2013]

• Word-Vectors (contd)
• GLOVE model (30 mins)
[GloVe: Global Vectors for Word Representation. Pennington et. al. EMNLP
2014]
• t-SNE (15 mins)
[Visualizing Data using t-SNE. Hinton et. al. 2008
How to Use t-SNE Effectively – Distill]
• Pros and Cons of using pre-trained word vectors (5 mins)
• Q & A (20 mins)

Module 3 (70 mins)
• Sentence2vec/Paragraph2vec/Doc2Vec
• Introduction (5 mins)
• PV-DM model (35 mins)
• PV-DBOW model
[Distributed representations of sentences and documents. Mikolov, et. al. ICML,
2014]
• Skip-Thoughts model (20 mins)
[Skip-Thought Vectors. Kiros et. al. arXiv preprint 2015]
• Pros and Cons (10 mins)

Module 4 (70 mins)
• Char2Vec
• Introduction (5 mins)
• Introduction to RNNs, LSTMs (20 mins)
• 1-hot Encoding (30 mins)
[The Unreasonable Effectiveness of Recurrent Neural Networks. Andrej Karpathy 2015]
• Character Embeddings (20 mins)
[Character-Aware Neural Language Models. Yoon Kim et. al. AAAI 2015]
• Pros and Cons (5 mins)
• Q & A (10 mins)

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Antimalarial drugs Malaria can occur if a mosquito infected with the Plasmodium parasite bites you. There are four kinds of malaria parasites that can infect humans: Plasmodium vivax, P. ovale, P. malariae, and P. falciparum. - P. falciparum causes a more severe form of the disease and those who contract this form of malaria have a higher risk of death. An infected mother can also pass the disease to her baby at birth. This is known as congenital malaria. Malaria is transmitted to humans by female mosquitoes of the genus Anopheles. Female mosquitoes take blood meals for egg production, and these blood meals are the link between the human and the mosquito hosts in the parasite life cycle. Whereas, Culicine mosquitoes such as Aedes spp. and Culex spp. are important vectors of other human pathogens including viruses and filarial worms, but have never been observed to transmit mammalian malarias. Malaria is transmitted by blood, so it can also be transmitted through: (i) an organ transplant; (ii) a transfusion; (iii) use of shared needles or syringes. Here's a comprehensive overview of **Antimalarial Drugs** including their **classification**, **mechanism of action (MOA)**, **structure-activity relationship (SAR)**, **uses**, and **side effects**—ideal for use in your **SlideShare PPT**: --- ## 🦠 **ANTIMALARIAL DRUGS OVERVIEW** --- ### ✅ **1. Classification of Antimalarial Drugs** #### **A. Based on Stage of Action:** * **Tissue Schizonticides**: Primaquine * **Blood Schizonticides**: Chloroquine, Artemisinin, Mefloquine * **Gametocytocides**: Primaquine, Artemisinin * **Sporontocides**: Pyrimethamine #### **B. Based on Chemical Class:** | Class | Examples | | ----------------------- | ------------------------ | | 4-Aminoquinolines | Chloroquine, Amodiaquine | | 8-Aminoquinolines | Primaquine, Tafenoquine | | Artemisinin Derivatives | Artesunate, Artemether | | Quinoline-methanols | Mefloquine | | Biguanides | Proguanil | | Sulfonamides | Sulfadoxine | | Antibiotics | Doxycycline, Clindamycin | | Naphthoquinones | Atovaquone | --- ### ⚙️ **2. Mechanism of Action (MOA)** | Drug/Class | MOA | | ----------------- | ----------------------------------------------------------------------- | | **Chloroquine** | Inhibits heme polymerization → toxic heme accumulation → parasite death | | **Artemisinin** | Generates free radicals → damages parasite proteins | | **Primaquine** | Disrupts mitochondrial function in liver stages | | **Mefloquine** | Disrupts heme detoxification pathway | | **Atovaquone** | Inhibits mitochondrial electron transport | | **Pyrimethamine** | Inhibits dihydrofolate reductase (

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Representation Learning for NLP

1. Representation Learning for NLP: Deep Dive Anuj Gupta, Satyam Saxena

2. • Duration : 6 hrs • Level : Intermediate to Advanced • Objective: For each of the topics, we will dig into the concepts, maths to build a theoretical understanding; followed by code (jupyter notebooks) to understand the implementation details.

3. Module 1 (30 mins) • Introduction to Text Representation (5 mins) • Old ways of representing text (20 mins) • Bag-Of-Words • TF–IDF • Co-occurrence matrix + SVD • Pros and Cons • Introduction to Embedding spaces (5 mins) Outline/Time Map - 4 Modules

4. Module 2 (160 mins) • Word-Vectors • Introduction + Bigram model (25 mins) • CBOW model (25 mins) • SKIP-GRAM model (25 mins) [Efficient estimation of word representations in vector space. Mikolov, et. al. ICLR Workshop, 2013] • Speed-Up (20 mins) • Negative Sampling • Hierarchical Softmax [Distributed representations of words and phrases and their compositionality. Mikolov, et. al. ANIPS, 2013]

5. • Word-Vectors (contd) • GLOVE model (30 mins) [GloVe: Global Vectors for Word Representation. Pennington et. al. EMNLP 2014] • t-SNE (15 mins) [Visualizing Data using t-SNE. Hinton et. al. 2008 How to Use t-SNE Effectively – Distill] • Pros and Cons of using pre-trained word vectors (5 mins) • Q & A (20 mins)

6. Module 3 (70 mins) • Sentence2vec/Paragraph2vec/Doc2Vec • Introduction (5 mins) • PV-DM model (35 mins) • PV-DBOW model [Distributed representations of sentences and documents. Mikolov, et. al. ICML, 2014] • Skip-Thoughts model (20 mins) [Skip-Thought Vectors. Kiros et. al. arXiv preprint 2015] • Pros and Cons (10 mins)

7. Module 4 (70 mins) • Char2Vec • Introduction (5 mins) • Introduction to RNNs, LSTMs (20 mins) • 1-hot Encoding (30 mins) [The Unreasonable Effectiveness of Recurrent Neural Networks. Andrej Karpathy 2015] • Character Embeddings (20 mins) [Character-Aware Neural Language Models. Yoon Kim et. al. AAAI 2015] • Pros and Cons (5 mins) • Q & A (10 mins)

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